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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-10-09
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">178</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
                    <span id="avg-score" class="font-semibold text-secondary">2.7</span>
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                <span id="display-count" class="font-medium">显示 178 篇论文 (共 178 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06732v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大语言模型是可靠的排序器吗？基于两阶段令牌优化的排序操纵
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tiancheng Xing, Jerry Li, Yixuan Du, Xiyang Hu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM作为排序器时的可靠性问题，核心提出两阶段令牌优化方法RAF，通过梯度引导和可读性约束生成自然文本扰动来操纵LLM的排序结果。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究LLM在搜索推荐系统中的排序可靠性问题，揭示了LLM排序器易受对抗性攻击的安全漏洞，对实际应用具有重要警示意义。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:40:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06732v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06732v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts. To expose this vulnerability, we present Rank Anything First (RAF), a two-stage token optimization method that crafts concise textual perturbations to consistently promote a target item in LLM-generated rankings while remaining hard to detect. Stage 1 uses Greedy Coordinate Gradient to shortlist candidate tokens at the current position by combining the gradient of the rank-target with a readability score; Stage 2 evaluates those candidates under exact ranking and readability losses using an entropy-based dynamic weighting scheme, and selects a token via temperature-controlled sampling. RAF generates ranking-promoting prompts token-by-token, guided by dual objectives: maximizing ranking effectiveness and preserving linguistic naturalness. Experiments across multiple LLMs show that RAF significantly boosts the rank of target items using naturalistic language, with greater robustness than existing methods in both promoting target items and maintaining naturalness. These findings underscore a critical security implication: LLM-based reranking is inherently susceptible to adversarial manipulation, raising new challenges for the trustworthiness and robustness of modern retrieval systems. Our code is available at: https://github.com/glad-lab/RAF.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06657v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于大语言模型的细粒度视频属性标注用于增强推荐系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何利用大型语言模型作为标注机制解决视频推荐系统中内容理解缺乏深度和细微差别的问题；核心方法是通过端到端工作流程使用LLM进行规模化、精细化的视频属性标注，并将这些丰富注释集成到在线推荐服务中。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM技术进行内容理解与标注，显著提升推荐系统性能，完全符合直接LLM应用和推荐系统核心进展的研究方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:17:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06657v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06657v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted development cycles and a lack of deep, nuanced comprehension. The "LLM-as-annotators" approach addresses these by significantly shortening development times and enabling the annotation of subtle attributes. This work details an end-to-end workflow encompassing: (1) iterative definition and robust evaluation of target attributes, refined by offline metrics and online A/B testing; (2) scalable offline bulk annotation of video corpora using LLMs with multimodal features, optimized inference, and knowledge distillation for broad application; and (3) integration of these rich annotations into the online recommendation serving system, for example, through personalized restrict retrieval. Experimental results demonstrate the efficacy of this approach, with LLMs outperforming human raters in offline annotation quality for nuanced attributes and yielding significant improvements of user participation and satisfied consumption in online A/B tests. The study provides insights into designing and scaling production-level LLM pipelines for rich content evaluation, highlighting the adaptability and benefits of LLM-generated nuanced understanding for enhancing content discovery, user satisfaction, and the overall effectiveness of modern recommendation systems.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07318v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>用于高效长上下文建模的人工海马体网络
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Artificial Hippocampus Networks for Efficient Long-Context Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunhao Fang, Weihao Yu, Shu Zhong, Qinghao Ye, Xuehan Xiong, Lai Wei
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究长序列建模中效率与准确性的权衡问题，核心思想是构建混合记忆框架：用滑动窗口保持短期无损记忆，同时通过人工海马网络将窗口外信息压缩为固定大小的长期记忆。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出结合滑动窗口KV缓存与可学习长期记忆模块的混合架构，直接解决Transformer长序列建模的效率瓶颈，属于Transformer架构效率优化的核心进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07318v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07318v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in attention-based Transformers. Inspired by the Multi-Store Model in cognitive science, we introduce a memory framework of artificial neural networks. Our method maintains a sliding window of the Transformer's KV cache as lossless short-term memory, while a learnable module termed Artificial Hippocampus Network (AHN) recurrently compresses out-of-window information into a fixed-size compact long-term memory. To validate this framework, we instantiate AHNs using modern RNN-like architectures, including Mamba2, DeltaNet, and Gated DeltaNet. Extensive experiments on long-context benchmarks LV-Eval and InfiniteBench demonstrate that AHN-augmented models consistently outperform sliding window baselines and achieve performance comparable or even superior to full-attention models, while substantially reducing computational and memory requirements. For instance, augmenting the Qwen2.5-3B-Instruct with AHNs reduces inference FLOPs by 40.5% and memory cache by 74.0%, while improving its average score on LV-Eval (128k sequence length) from 4.41 to 5.88. Code is available at: https://github.com/ByteDance-Seed/AHN.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07230v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Customer-R1：基于强化学习的LLM智能体在在线购物中实现个性化人类行为模拟
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Customer-R1: Personalized Simulation of Human Behaviors via RL-based LLM Agent in Online Shopping
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Jing Huang, Dakuo Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何让LLM代理更好地模拟个性化用户行为；核心方法是通过强化学习训练基于明确用户画像的LLM代理，优化下一步推理和动作生成以实现个性化行为模拟。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对个性化用户行为模拟这一推荐系统核心问题，提出了基于强化学习的LLM代理方法，完美契合个性化推荐和LLM应用的研究重点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:00:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07230v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07230v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level policy without conditioning on a user's persona, yielding generic rather than personalized simulations. In this work, we pose a critical question: how can LLM agents better simulate personalized user behavior? We introduce Customer-R1, an RL-based method for personalized, step-wise user behavior simulation in online shopping environments. Our policy is conditioned on an explicit persona, and we optimize next-step rationale and action generation via action correctness reward signals. Experiments on the OPeRA dataset emonstrate that Customer-R1 not only significantly outperforms prompting and SFT-based baselines in next-action prediction tasks, but also better matches users' action distribution, indicating higher fidelity in personalized behavior simulation.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07048v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Search-R3：在大型语言模型中统一推理与嵌入生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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        <div class="mb-2 text-base text-gray-700">
            Search-R3: Unifying Reasoning and Embedding Generation in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuntao Gui, James Cheng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何将大语言模型适配于检索任务；核心方法是利用链式思维推理过程直接生成搜索嵌入，通过监督学习和强化学习统一推理与嵌入生成。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接统一LLM推理与检索嵌入生成，完全契合搜索领域核心进展和LLM直接应用方向，方法创新性强。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:16:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07048v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07048v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">I.2.7</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We present Search-R3, a novel framework that addresses this limitation by adapting LLMs to generate search embeddings as a direct output of their reasoning process. Our approach exploits LLMs' chain-of-thought capabilities, allowing them to produce more effective embeddings by reasoning step-by-step through complex semantic analyses. We implement this through three complementary mechanisms. (1) a supervised learning stage enables the model's ability to produce quality embeddings, (2) a reinforcement learning (RL) methodology that optimizes embedding generation alongside reasoning, and (3) a specialized RL environment that efficiently handles evolving embedding representations without requiring complete corpus re-encoding at each training iteration. Our extensive evaluations on diverse benchmarks demonstrate that Search-R3 significantly outperforms prior methods by unifying the reasoning and embedding generation processes. This integrated post-training approach represents a substantial advancement in handling complex knowledge-intensive tasks that require both sophisticated reasoning and effective information retrieval. Project page: https://github.com/ytgui/Search-R3
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07019v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>原生混合注意力机制用于高效序列建模
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Native Hybrid Attention for Efficient Sequence Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jusen Du, Jiaxi Hu, Tao Zhang, Weigao Sun, Yu Cheng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究Transformer在长序列建模中的效率问题，核心方法是提出原生混合注意力架构，通过线性RNN维护长期上下文和滑动窗口捕获短期token，在统一层设计中实现线性与全注意力的混合。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对Transformer架构效率问题提出混合注意力机制，属于核心的Transformer技术进步，对推荐系统和搜索中的长序列建模有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:44:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07019v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07019v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. In this work, we introduce Native Hybrid Attention (NHA), a novel hybrid architecture of linear and full attention that integrates both intra \& inter-layer hybridization into a unified layer design. NHA maintains long-term context in key-value slots updated by a linear RNN, and augments them with short-term tokens from a sliding window. A single \texttt{softmax attention} operation is then applied over all keys and values, enabling per-token and per-head context-dependent weighting without requiring additional fusion parameters. The inter-layer behavior is controlled through a single hyperparameter, the sliding window size, which allows smooth adjustment between purely linear and full attention while keeping all layers structurally uniform. Experimental results show that NHA surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks. Furthermore, pretrained LLMs can be structurally hybridized with NHA, achieving competitive accuracy while delivering significant efficiency gains. Code is available at https://github.com/JusenD/NHA.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06774v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于动态逻辑求解器组合的自适应大语言模型符号推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lei Xu, Pierre Beckmann, Marco Valentino, André Freitas
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何动态整合LLM与形式逻辑求解器进行神经符号推理，核心思想是通过自动识别自然语言问题中的推理策略，动态选择和组合专门的逻辑求解器来实现自适应多范式推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的动态逻辑求解器组合框架直接属于LLM符号推理的核心技术进展，其自适应推理机制对推荐系统和搜索中的复杂推理任务具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:57:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06774v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06774v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expressed in natural language; and (2) dynamically selects and applies specialized formal logical solvers via autoformalization interfaces. Extensive experiments on individual and multi-paradigm reasoning tasks support the following conclusions: LLMs are effective at predicting the necessary formal reasoning strategies with an accuracy above 90 percent. This enables flexible integration with formal logical solvers, resulting in our framework outperforming competing baselines by 27 percent and 6 percent compared to GPT-4o and DeepSeek-V3.1, respectively. Moreover, adaptive reasoning can even positively impact pure LLM methods, yielding gains of 10, 5, and 6 percent on zero-shot, CoT, and symbolic CoT settings with GPT-4o. Finally, although smaller models struggle with adaptive neuro-symbolic reasoning, post-training offers a viable path to improvement. Overall, this work establishes the foundations for adaptive LLM-symbolic reasoning, offering a path forward for unifying material and formal inferences on heterogeneous reasoning challenges.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06727v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于端到端摘要化上下文管理的可扩展大语言模型多轮强化学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, Jiecao Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究解决LLM在多轮工具使用中的上下文长度瓶颈问题，核心思想是通过LLM生成的摘要周期性压缩工具使用历史，保留任务相关信息，实现端到端优化工具使用行为和摘要策略。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对LLM多轮交互中的上下文长度瓶颈，提出了端到端的摘要式上下文管理方法，直接解决了推荐和搜索系统中长期用户交互的核心挑战。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:29:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06727v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06727v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with \underline{SU}mmarization augmented \underline{P}olicy \underline{O}ptimization (\texttt{SUPO}), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that \texttt{SUPO} significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks, \texttt{SUPO} can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time. Our results establish summarization-based context management as a principled and scalable approach for training RL agents beyond a fixed context length limit.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06640v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>状态空间与Transformer架构中上下文表征流动的对比分析
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Min-Yen Kan, Luu Anh Tuan
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究状态空间模型和Transformer架构中上下文表示传播的核心差异问题。核心发现是Transformer早期快速同质化表示而后期重新分化，状态空间模型则相反，这种差异源于架构设计vs训练动态的不同归纳偏置。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文深入分析Transformer架构的表示传播机制和归纳偏置，直接关联Transformer技术进展，对推荐和搜索系统的长序列建模具有重要启示。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:46:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06640v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06640v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing, offering linear scaling and lower memory use. Yet, how contextual information flows across layers and tokens in these architectures remains understudied. We present the first unified, token- and layer-level analysis of representation propagation in SSMs and TBMs. Using centered kernel alignment, stability metrics, and probing, we characterize how representations evolve within and across layers. We find a key divergence: TBMs rapidly homogenize token representations, with diversity reemerging only in later layers, while SSMs preserve token uniqueness early but converge to homogenization deeper. Theoretical analysis and parameter randomization further reveal that oversmoothing in TBMs stems from architectural design, whereas in SSMs it arises mainly from training dynamics. These insights clarify the inductive biases of both architectures and inform future model and training designs for long-context reasoning.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07248v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>不要为工具适配小型语言模型；将工具模式适配到模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jonggeun Lee, Woojung Song, Jongwook Han, Haesung Pyun, Yohan Jo
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究小语言模型在工具使用任务中因模式错配导致的性能问题，核心思想是通过基于预训练熟悉度的模式生成方法，将工具模式适配到模型的知识结构而非反向适配模型。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出通过适配工具模式而非模型来解决SLM工具使用的核心问题，这种模式对齐方法对推荐和搜索系统中的工具集成具有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:16:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07248v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07248v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Small language models (SLMs) offer significant computational advantages for tool-augmented AI systems, yet they struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters. A common failure mode is schema misalignment: models hallucinate plausible but non-existent tool names that reflect naming conventions internalized during pretraining but absent from the provided tool schema. Rather than forcing models to adapt to arbitrary schemas, we propose adapting schemas to align with models' pretrained knowledge. We introduce PA-Tool (Pretraining-Aligned Tool Schema Generation), a training-free method that leverages peakedness-a signal from contamination detection indicating pretraining familiarity-to automatically rename tool components. By generating multiple candidates and selecting those with highest output concentration across samples, PA-Tool identifies pretrain-aligned naming patterns. Experiments on MetaTool and RoTBench show improvements of up to 17% points, with schema misalignment errors reduced by 80%. PA-Tool enables small models to approach state-of-the-art performance while maintaining computational efficiency for adaptation to new tools without retraining. Our work demonstrates that schema-level interventions can unlock the tool-use potential of resource-efficient models by adapting schemas to models rather than models to schemas.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07118v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>TRIM：基于逐词注意力推导显著性的数据高效指令调优
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manish Nagaraj, Sakshi Choudhary, Utkarsh Saxena, Deepak Ravikumar, Kaushik Roy
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何高效选择高质量的指令调优数据子集；核心方法是利用前向传播中的注意力机制生成token级特征指纹，通过模式匹配识别最具代表性的训练样本，避免传统梯度方法的计算开销。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出基于注意力指纹的高效指令调优数据选择方法，直接属于LLM核心技术进步领域，对推荐和搜索系统的数据效率优化具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:11:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07118v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07118v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Instruction tuning is essential for aligning large language models (LLMs) to downstream tasks and commonly relies on large, diverse corpora. However, small, high-quality subsets, known as coresets, can deliver comparable or superior results, though curating them remains challenging. Existing methods often rely on coarse, sample-level signals like gradients, an approach that is computationally expensive and overlooks fine-grained features. To address this, we introduce TRIM (Token Relevance via Interpretable Multi-layer Attention), a forward-only, token-centric framework. Instead of using gradients, TRIM operates by matching underlying representational patterns identified via attention-based "fingerprints" from a handful of target samples. Such an approach makes TRIM highly efficient and uniquely sensitive to the structural features that define a task. Coresets selected by our method consistently outperform state-of-the-art baselines by up to 9% on downstream tasks and even surpass the performance of full-data fine-tuning in some settings. By avoiding expensive backward passes, TRIM achieves this at a fraction of the computational cost. These findings establish TRIM as a scalable and efficient alternative for building high-quality instruction-tuning datasets.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06870v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>λ-GRPO：通过可学习令牌偏好统一GRPO框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            $λ$-GRPO: Unifying the GRPO Frameworks with Learnable Token Preferences
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yining Wang, Jinman Zhao, Chuangxin Zhao, Shuhao Guan, Gerald Penn, Shinan Liu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究GRPO强化学习框架中的长度偏差问题，核心思想是通过引入可学习的参数λ来自适应控制token级权重，统一现有方法并让模型在优化过程中学习自身的token偏好。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出可学习的token偏好参数来解决GRPO框架中的长度偏差问题，直接改进强化学习优化方法，对LLM推理能力提升有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:39:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06870v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06870v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement Learning with Human Feedback (RLHF) has been the dominant approach for improving the reasoning capabilities of Large Language Models (LLMs). Recently, Reinforcement Learning with Verifiable Rewards (RLVR) has simplified this paradigm by replacing the reward and value models with rule-based verifiers. A prominent example is Group Relative Policy Optimization (GRPO). However, GRPO inherently suffers from a length bias, since the same advantage is uniformly assigned to all tokens of a response. As a result, longer responses distribute the reward over more tokens and thus contribute disproportionately to gradient updates. Several variants, such as DAPO and Dr. GRPO, modify the token-level aggregation of the loss, yet these methods remain heuristic and offer limited interpretability regarding their implicit token preferences. In this work, we explore the possibility of allowing the model to learn its own token preference during optimization. We unify existing frameworks under a single formulation and introduce a learnable parameter $\lambda$ that adaptively controls token-level weighting. We use $\lambda$-GRPO to denote our method, and we find that $\lambda$-GRPO achieves consistent improvements over vanilla GRPO and DAPO on multiple mathematical reasoning benchmarks. On Qwen2.5 models with 1.5B, 3B, and 7B parameters, $\lambda$-GRPO improves average accuracy by $+1.9\%$, $+1.0\%$, and $+1.7\%$ compared to GRPO, respectively. Importantly, these gains come without any modifications to the training data or additional computational cost, highlighting the effectiveness and practicality of learning token preferences.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06826v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大型语言模型中期训练：综述
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Mid-Training of Large Language Models: A Survey
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaixiang Mo, Yuxin Shi, Weiwei Weng, Zhiqiang Zhou, Shuman Liu, Haibo Zhang, Anx...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究大型语言模型训练过程中中间阶段的系统化优化问题，核心思想是通过数据质量精炼、优化调度调整和上下文长度扩展等多阶段退火式训练，提升模型泛化能力和抽象能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统研究LLM训练中的中间阶段优化方法，直接关联核心LLM技术进步，对推荐和搜索系统的大模型训练具有重要指导意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:49:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06826v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06826v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are typically developed through large-scale pre-training followed by task-specific fine-tuning. Recent advances highlight the importance of an intermediate mid-training stage, where models undergo multiple annealing-style phases that refine data quality, adapt optimization schedules, and extend context length. This stage mitigates diminishing returns from noisy tokens, stabilizes convergence, and expands model capability in late training. Its effectiveness can be explained through gradient noise scale, the information bottleneck, and curriculum learning, which together promote generalization and abstraction. Despite widespread use in state-of-the-art systems, there has been no prior survey of mid-training as a unified paradigm. We introduce the first taxonomy of LLM mid-training spanning data distribution, learning-rate scheduling, and long-context extension. We distill practical insights, compile evaluation benchmarks, and report gains to enable structured comparisons across models. We also identify open challenges and propose avenues for future research and practice.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06825v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于模型即工具与强化学习的自适应工具生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Adaptive Tool Generation with Models as Tools and Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang, Lei Zhang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究工具增强语言模型的扩展性和可靠性挑战，核心方法是构建多智能体框架通过模拟观察和结构化推理轨迹来训练模型，无需依赖实时API交互。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出用模拟训练替代实时API调用的工具增强LLM框架，直接解决了搜索和推荐系统中大规模部署的扩展性和可靠性问题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:48:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06825v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06825v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-validated, simulated observations. Our approach operates through a multi-agent architecture where a ToolMaker generates task-specific, OpenAI-compatible tool interfaces, an AutoAgent produces structured think-act-observe sequences, and a ToolActor simulates realistic responses. Training proceeds in two stages: Stage-1 Supervised Fine-Tuning (SFT) teaches 'trace grammar' from complete reasoning sequences; Stage-2 Group Relative Policy Optimization (GRPO) optimizes strategy with a composite trace reward that balances answer correctness and internal consistency. Across four multi-hop QA benchmarks (HotpotQA, MuSiQue, 2WikiMultiHopQA, Bamboogle), MTR attains competitive Exact Match (EM) scores to live-API systems and excels on reasoning-intensive tasks, suggesting that effective tool reasoning can be learned from structured traces without live interactions.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06750v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Gold-Switch：无需训练即可实现慢思考与快思考大语言模型的叠加
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Gold-Switch: Training-Free Superposition of Slow- and Fast- Thinking LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jaeseong Lee, Dayoung Kwon, seung-won hwang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大型推理模型在结构化任务中过度思考导致性能下降和资源浪费的问题，其核心方法是通过分析奇异值累积能量，识别最优低秩投影，在推理时选择性遗忘LRM知识来调整推理强度。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的训练免费推理优化方法直接适用于搜索推荐系统的部署效率问题，其核心思想与Transformer架构优化高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:17:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06750v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06750v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Reasoning Models (LRMs) excel in structured tasks by emulating deliberate human reasoning but often suffer from overthinking, degrading performance and wasting resources. One possible baseline is to deploy both LLM and LRM, then route input by predicting whether it requires reasoning and may cause overthinking. However, deploying multiple models can be costly or impractical. We propose a superposed deployment strategy with a lightweight, training-free regulation to optimize inference by switching one model on and off. Instead of routing, we selectively unlearn from LRM at inference, scaling down computation while preserving reasoning. By analyzing the cumulative energy of singular values, we identify optimal low-rank projections to adjust reasoning just right.
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            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06728v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>神经排序器中词频计算的因果洞察复现与扩展
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cile van Marken, Roxana Petcu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究神经排序模型如何内部计算文档相关性，核心方法是采用激活修补的因果可解释性框架，定位模型中编码词频信息和检索公理的特定组件。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过因果干预方法深入分析神经排序模型的内部决策机制，直接关联推荐系统模型可解释性这一核心领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:29:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06728v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06728v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Neural ranking models have shown outstanding performance across a variety of tasks, such as document retrieval, re-ranking, question answering and conversational retrieval. However, the inner decision process of these models remains largely unclear, especially as models increase in size. Most interpretability approaches, such as probing, focus on correlational insights rather than establishing causal relationships. The paper 'Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models' by Chen et al. addresses this gap by introducing a framework for activation patching - a causal interpretability method - in the information retrieval domain, offering insights into how neural retrieval models compute document relevance. The study demonstrates that neural ranking models not only capture term-frequency information, but also that these representations can be localized to specific components of the model, such as individual attention heads or layers. This paper aims to reproduce the findings by Chen et al. and to further explore the presence of pre-defined retrieval axioms in neural IR models. We validate the main claims made by Chen et al., and extend the framework to include an additional term-frequency axiom, which states that the impact of increasing query term frequency on document ranking diminishes as the frequency becomes higher. We successfully identify a group of attention heads that encode this axiom and analyze their behavior to give insight into the inner decision-making process of neural ranking models.
                </div>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07147v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>用于有状态推理时搜索的多智能体框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Multi-Agent Framework for Stateful Inference-Time Search
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arshika Lalan, Rajat Ghosh, Aditya Kolsur, Debojyoti Dutta
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多步推理任务中无状态推理的局限性问题，核心思想是结合持久推理状态、对抗性变异和进化保留，构建状态保持的多智能体进化搜索框架来提升复杂推理能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的状态保持多智能体进化搜索框架直接属于推理时搜索技术，对搜索和推荐系统的复杂多步推理有重要借鉴价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:48:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07147v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07147v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span><span class="category-tag">cs.SE</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent work explores agentic inference-time techniques to perform structured, multi-step reasoning. However, stateless inference often struggles on multi-step tasks due to the absence of persistent state. Moreover, task-specific fine-tuning or instruction-tuning often achieve surface-level code generation but remain brittle on tasks requiring deeper reasoning and long-horizon dependencies. To address these limitations, we propose stateful multi-agent evolutionary search, a training-free framework that departs from prior stateless approaches by combining (i) persistent inference-time state, (ii) adversarial mutation, and (iii) evolutionary preservation. We demonstrate its effectiveness in automated unit test generation through the generation of edge cases. We generate robust edge cases using an evolutionary search process, where specialized agents sequentially propose, mutate, and score candidates. A controller maintains persistent state across generations, while evolutionary preservation ensures diversity and exploration across all possible cases. This yields a generalist agent capable of discovering robust, high-coverage edge cases across unseen codebases. Experiments show our stateful multi-agent inference framework achieves substantial gains in coverage over stateless single-step baselines, evaluated on prevalent unit-testing benchmarks such as HumanEval and TestGenEvalMini and using three diverse LLM families - Llama, Gemma, and GPT. These results indicate that combining persistent inference-time state with evolutionary search materially improves unit-test generation.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06747v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>TWIST：基于大语言模型迭代向量更新的免训练免标签短文本聚类方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            TWIST: Training-free and Label-free Short Text Clustering through Iterative Vector Updating with LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>I-Fan Lin, Faegheh Hasibi, Suzan Verberne
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究无监督短文本聚类问题，核心思想是通过LLM指导的迭代向量更新方法，构建稀疏向量并不断优化，实现无需训练数据和标签的聚类。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出无训练无标签的短文本聚类方法，直接应用于推荐系统用户意图分析场景，与搜索推荐领域高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:05:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06747v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06747v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this paper, we propose a training-free and label-free method for short text clustering that can be used on top of any existing embedder. In the context of customer-facing chatbots, companies are dealing with large amounts of user utterances that need to be clustered according to their intent. In these commercial settings, no labeled data is typically available, and the number of clusters is not known. Our method is based on iterative vector updating: it constructs sparse vectors based on representative texts, and then iteratively refines them through LLM guidance. Our method achieves comparable or superior results to state-of-the-art methods that use contrastive learning, but without assuming prior knowledge of clusters or labels. Experiments on diverse datasets and smaller LLMs show that our method is model agnostic and can be applied to any embedder, with relatively small LLMs, and different clustering methods. We also show that our method scales to large datasets, reducing the computational cost of the LLM. These low-resource, adaptable settings and the scalability of our method make it more aligned with real-world scenarios than existing clustering methods.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06695v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>学习重写提示以引导大型语言模型在下游任务上进行自举学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qinhao Zhou, Xiang Xiang, Kun He, John E. Hopcroft
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究机器翻译等下游任务中提示优化的局限性问题，核心思想是使用基于反向翻译策略训练的小参数模型专门优化提示的输入组件，而非传统的指令组件优化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的小参数模型提示重写方法直接应用于下游任务优化，属于LLM在具体应用中的直接应用范畴，与推荐和搜索系统的任务优化高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:40:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06695v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06695v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.AS</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. Prompts for LLMs generally comprise two components: the \textit{instruction}, which defines the task or objective, and the \textit{input}, which is tailored to the instruction type. In natural language generation (NLG) tasks such as machine translation, the \textit{input} component is particularly critical, while the \textit{instruction} component tends to be concise. Existing prompt engineering methods primarily focus on optimizing the \textit{instruction} component for general tasks, often requiring large-parameter LLMs as auxiliary tools. However, these approaches exhibit limited applicability for tasks like machine translation, where the \textit{input} component plays a more pivotal role. To address this limitation, this paper introduces a novel prompt optimization method specifically designed for machine translation tasks. The proposed approach employs a small-parameter model trained using a back-translation-based strategy, significantly reducing training overhead for single-task optimization while delivering highly effective performance. With certain adaptations, this method can also be extended to other downstream tasks.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07227v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>从何处开始：通过子网络选择与蒸馏实现高效预训练
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik, Gabriela Kadle...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究小语言模型预训练效率问题，核心思想是通过结构稀疏子网络初始化、进化搜索和知识蒸馏相结合的方法，为SLM预训练提供更好的起点并加速训练过程。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦小语言模型高效预训练方法，虽然不直接涉及推荐系统或广告，但其子网络选择和知识蒸馏技术对资源受限的推荐模型优化具有潜在借鉴价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:57:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07227v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07227v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Small Language models (SLMs) offer an efficient and accessible alternative to Large Language Models (LLMs), delivering strong performance while using far fewer resources. We introduce a simple and effective framework for pretraining SLMs that brings together three complementary ideas. First, we identify structurally sparse sub-network initializations that consistently outperform randomly initialized models of similar size under the same compute budget. Second, we use evolutionary search to automatically discover high-quality sub-network initializations, providing better starting points for pretraining. Third, we apply knowledge distillation from larger teacher models to speed up training and improve generalization. Together, these components make SLM pretraining substantially more efficient: our best model, discovered using evolutionary search and initialized with LLM weights, matches the validation perplexity of a comparable Pythia SLM while requiring 9.2x fewer pretraining tokens. We release all code and models at https://github.com/whittle-org/whittle/, offering a practical and reproducible path toward cost-efficient small language model development at scale.
                </div>
            </details>
    </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06652v1" target="_blank" rel="noopener noreferrer">
                基于完全自合成数据对齐大型语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Aligning Large Language Models via Fully Self-Synthetic Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Yu Meng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及LLM对齐技术，这是LLM基础进展的核心方向。在推荐系统、搜索和广告领域，自合成数据对齐可以显著提升LLM在特定任务上的性能表现，例如生成更准确的推荐理由、优化搜索相关性排序或改进广告文案生成质量，而无需依赖大量人工标注数据。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:07:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06652v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06652v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Traditional reinforcement learning from human feedback (RLHF) for large language models (LLMs) relies on expensive human-annotated datasets, while Reinforcement Learning from AI Feedback (RLAIF) also incurs significant costs, requiring the collection of diverse prompts and corresponding responses, often necessitating external reward models or proprietary models like GPT-4 to annotate preference pairs. In this work, we introduce Self-Alignment Optimization (SAO), a fully self-synthetic framework for LLM alignment, where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. Specifically, SAO first instructs the LLM to engage in persona role-play and generate diverse prompts and responses, which are then self-evaluated for preference optimization. Extensive experiments demonstrate that SAO effectively enhances the model's chat capabilities on standard benchmarks like AlpacaEval~2.0, while maintaining strong performance on downstream objective tasks (e.g., question-answering, math reasoning). Our work provides a practical solution for self-improvement in aligning LLMs, and the code for reproducing our results is available at: https://github.com/SJY8460/SAO.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06552v1" target="_blank" rel="noopener noreferrer">
                翻转对话：训练与评估用户语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Flipping the Dialogue: Training and Evaluating User Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tarek Naous, Philippe Laban, Wei Xu, Jennifer Neville
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于用户语言模型的训练与评估，这直接属于'直接LLM应用'范畴，在推荐系统和搜索领域具有明确应用价值。用户语言模型可以用于理解用户意图、个性化对话交互，以及改进搜索查询理解和推荐系统的人机交互体验。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 01:04:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06552v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06552v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Conversations with LMs involve two participants: a human user leading the conversation, and an LM assistant responding to the user's request. To satisfy this specific role, LMs are post-trained to be helpful assistants -- optimized to produce exhaustive and well-structured responses, free of ambiguity and grammar errors. User utterances, on the other hand, are rarely perfected, with each user phrasing requests in unique ways, sometimes putting in partial effort at each turn and refining on the fly. To evaluate LM performance in realistic settings, prior work simulated users in multi-turn conversations, often prompting an LLM originally trained to be a helpful assistant to act as a user. However, we show that assistant LMs make for poor user simulators, with the surprising finding that better assistants yield worse simulators. Instead, we introduce purpose-built User Language Models (User LMs) - models post-trained to simulate human users in multi-turn conversations. Through various evaluations, we show how User LMs align better with human behavior and achieve better simulation robustness than existing simulation methods. When leveraging User LMs to simulate coding and math conversations, the performance of a strong assistant (GPT-4o) drops from 74.6% to 57.4%, confirming that more realistic simulation environments lead to assistant struggles as they fail to cope with the nuances of users in multi-turn setups.
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            <a href="https://www.alphaxiv.org/abs/2510.06548v1" target="_blank" rel="noopener noreferrer">
                从加速到饱和：自举语言模型预训练的缩放行为
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            From Acceleration to Saturation: Scaling Behavior of Bootstrapped Language Model Pretraining
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seng Pei Liew, Takuya Kato
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究语言模型预训练的缩放行为，这属于'赋能LLM技术'范畴，对推荐系统、搜索和广告领域具有重要应用价值。理解语言模型的缩放规律可以帮助优化大规模推荐和搜索系统的模型训练效率与成本，为实际业务部署提供关键指导。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 00:59:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06548v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06548v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Bootstrapped pretraining, i.e., the reuse of a pretrained base model for further pretraining, such as continual pretraining or model growth, is promising at reducing the cost of training language models from scratch. However, its effectiveness remains unclear, especially when applied to overtrained base models. In this work, we empirically study the scaling behavior of bootstrapped pretraining and find that its scaling efficiency diminishes in a predictable manner: The scaling exponent with respect to second-stage pretraining tokens decreases logarithmically with the number of tokens used to pretrain the base model. The joint dependence on first- and second-stage tokens is accurately modeled by a simple scaling law. Such saturation effect reveals a fundamental trade-off in multi-stage pretraining strategies: the more extensively a model is pretrained, the less additional benefit bootstrapping provides. Our findings provide practical insights for efficient language model training and raise important considerations for the reuse of overtrained models.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.07058v1" target="_blank" rel="noopener noreferrer">
                概念检索——是什么以及如何实现？
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Concept Retrieval -- What and How?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ori nizan, Oren Shrout, Ayellet Tal
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">概念检索直接涉及搜索系统的核心功能，专注于理解用户查询背后的概念意图而非字面匹配。这种技术可以显著提升搜索和推荐系统的语义理解能力，通过更好的概念映射来改进相关性排序和个性化推荐。在广告领域，概念检索可以帮助更准确地理解广告意图与用户需求的匹配度。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:26:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07058v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07058v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A concept may reflect either a concrete or abstract idea. Given an input image, this paper seeks to retrieve other images that share its central concepts, capturing aspects of the underlying narrative. This goes beyond conventional retrieval or clustering methods, which emphasize visual or semantic similarity. We formally define the problem, outline key requirements, and introduce appropriate evaluation metrics. We propose a novel approach grounded in two key observations: (1) While each neighbor in the embedding space typically shares at least one concept with the query, not all neighbors necessarily share the same concept with one another. (2) Modeling this neighborhood with a bimodal Gaussian distribution uncovers meaningful structure that facilitates concept identification. Qualitative, quantitative, and human evaluations confirm the effectiveness of our approach. See the package on PyPI: https://pypi.org/project/coret/
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06982v1" target="_blank" rel="noopener noreferrer">
                重新审视Mixout：一条被忽视的通往鲁棒微调的路径
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Revisiting Mixout: An Overlooked Path to Robust Finetuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">Mixout作为一种正则化技术，通过随机混合预训练和微调参数来防止过拟合，这对LLM在推荐/搜索系统中的微调稳定性至关重要。该技术能提升模型在领域迁移和冷启动场景下的鲁棒性，直接适用于广告、搜索等需要频繁微调的业务场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:07:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06982v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06982v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that intermittently replaces finetuned weights with their pretrained reference, through the lens of a single-run, weight-sharing implicit ensemble. This perspective reveals three key levers that govern robustness: the \emph{masking anchor}, \emph{resampling frequency}, and \emph{mask sparsity}. Guided by this analysis, we introduce GMixout, which (i) replaces the fixed anchor with an exponential moving-average snapshot that adapts during training, and (ii) regulates masking period via an explicit resampling-frequency hyperparameter. Our sparse-kernel implementation updates only a small fraction of parameters with no inference-time overhead, enabling training on consumer-grade GPUs. Experiments on benchmarks covering covariate shift, corruption, and class imbalance, ImageNet / ImageNet-LT, DomainNet, iWildCam, and CIFAR100-C, GMixout consistently improves in-domain accuracy beyond zero-shot performance while surpassing both Model Soups and strong parameter-efficient finetuning baselines under distribution shift.
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            <a href="https://www.alphaxiv.org/abs/2510.06820v1" target="_blank" rel="noopener noreferrer">
                面向大规模视觉语言重排序的高效判别式联合编码器
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mitchell Keren Taraday, Shahaf Wagner, Chaim Baskin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及视觉语言联合编码和重排序技术，直接类比于VLM处理异构数据的理念，可应用于搜索和推荐系统中处理多模态内容（如图文商品、视频推荐）。高效的联合编码器设计对大规模推荐系统的实时性至关重要，判别式方法能提升排序精度。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:46:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06820v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06820v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision--language rerankers are largely absent. We find that seminal joint encoders such as BLIP are severely bottlenecked by an expensive visual feature-extraction stage, preventing practical deployment at scale. Motivated by this bottleneck, we introduce EDJE, an Efficient Discriminative Joint Encoder that precomputes vision tokens offline and compresses them via a lightweight attention-based adapter, so online inference runs only a compact joint encoder over a small set of visual tokens plus the text. EDJE preserves strong retrieval performance while drastically reducing storage and online compute, enabling high-throughput inference. Specifically, EDJE processes 50k image--text pairs/second while requiring 49kB of disk storage per image, matching prior art on Flickr (zero-shot) and COCO (fine-tuned) retrieval. The implementation and checkpoints will be made publicly available shortly.
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            <a href="https://www.alphaxiv.org/abs/2510.06673v1" target="_blank" rel="noopener noreferrer">
                七足怪：基于视觉信号的语言建模
            </a>
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            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Heptapod: Language Modeling on Visual Signals
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongxin Zhu, Jiawei Chen, Yuanzhe Chen, Zhuo Chen, Dongya Jia, Jian Cong, Xiaobi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探索视觉信号的语言建模，类似于视觉语言模型(VLM)方法，可直接应用于搜索和推荐系统中的多模态内容理解。将视觉特征作为语言建模的输入模态，这种统一建模方法可处理推荐和广告中的异构数据（如图像、文本、用户行为序列），实现跨模态表征学习。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:54:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06673v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06673v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce Heptapod, an image autoregressive model that adheres to the foundational principles of language modeling. Heptapod employs \textbf{causal attention}, \textbf{eliminates reliance on CFG}, and \textbf{eschews the trend of semantic tokenizers}. Our key innovation is \textit{next 2D distribution prediction}: a causal Transformer with reconstruction-focused visual tokenizer, learns to predict the distribution over the entire 2D spatial grid of images at each timestep. This learning objective unifies the sequential modeling of autoregressive framework with the holistic self-supervised learning of masked autoencoding, enabling the model to capture comprehensive image semantics via generative training. On the ImageNet generation benchmark, Heptapod achieves an FID of $2.70$, significantly outperforming previous causal autoregressive approaches. We hope our work inspires a principled rethinking of language modeling on visual signals and beyond.
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            <a href="https://www.alphaxiv.org/abs/2510.06999v1" target="_blank" rel="noopener noreferrer">
                面向大型法律数据集的RAG系统可靠检索研究
            </a>
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            <i class="fa fa-star mr-1"></i>7/10
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        <div class="mb-2 text-base text-gray-700">
            Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Markus Reuter, Tobias Lingenberg, Rūta Liepiņa, Francesca Lagioia, Marco Lippi, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于RAG系统的检索可靠性，这直接适用于搜索和推荐系统的核心组件。虽然以法律数据集为背景，但RAG系统的检索改进技术可以泛化到搜索和推荐中的文档检索、候选集生成等关键环节，提升系统准确性和可靠性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:22:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06999v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06999v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span><span class="category-tag">I.2.7; H.3.3; K.5.0</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval systems to fail. In this paper, we address this challenge by first identifying and quantifying a critical failure mode we term Document-Level Retrieval Mismatch (DRM), where the retriever selects information from entirely incorrect source documents. To mitigate DRM, we investigate a simple and computationally efficient technique which we refer to as Summary-Augmented Chunking (SAC). This method enhances each text chunk with a document-level synthetic summary, thereby injecting crucial global context that would otherwise be lost during a standard chunking process. Our experiments on a diverse set of legal information retrieval tasks show that SAC greatly reduces DRM and, consequently, also improves text-level retrieval precision and recall. Interestingly, we find that a generic summarization strategy outperforms an approach that incorporates legal expert domain knowledge to target specific legal elements. Our work provides evidence that this practical, scalable, and easily integrable technique enhances the reliability of RAG systems when applied to large-scale legal document datasets.
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            <a href="https://www.alphaxiv.org/abs/2510.07213v1" target="_blank" rel="noopener noreferrer">
                语言存在于稀疏维度：面向大语言模型的可解释且高效的多语言控制
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            <i class="fa fa-star mr-1"></i>7/10
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        <div class="mb-2 text-base text-gray-700">
            Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chengzhi Zhong, Fei Cheng, Qianying Liu, Yugo Murawaki, Chenhui Chu, Sadao Kuroh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及大语言模型的多语言控制和稀疏维度技术，这属于'使能LLM技术'范畴。稀疏维度方法可以应用于推荐系统和搜索中的多语言内容理解与个性化控制，提高模型在跨语言场景下的效率和可解释性，对国际化平台的推荐和搜索有直接应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:46:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07213v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07213v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multilingual content into English-aligned representations at intermediate layers and then project them back into target-language token spaces in the final layer. From this observation, we hypothesize that this cross-lingual transition is governed by a small and sparse set of dimensions, which occur at consistent indices across the intermediate to final layers. Building on this insight, we introduce a simple, training-free method to identify and manipulate these dimensions, requiring only as few as 50 sentences of either parallel or monolingual data. Experiments on a multilingual generation control task reveal the interpretability of these dimensions, demonstrating that the interventions in these dimensions can switch the output language while preserving semantic content, and that it surpasses the performance of prior neuron-based approaches at a substantially lower cost.
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            <a href="https://www.alphaxiv.org/abs/2510.06754v1" target="_blank" rel="noopener noreferrer">
                UniFField：一种可泛化的统一神经特征场，用于任意场景中的视觉、语义和空间不确定性建模
            </a>
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            <i class="fa fa-star mr-1"></i>7/10
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            UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christian Maurer, Snehal Jauhri, Sophie Lueth, Georgia Chalvatzaki
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的统一神经特征场技术能够处理视觉、语义和空间不确定性，这属于Transformer架构的效率和新注意力机制方面的进展。在推荐系统和搜索领域，这种技术可以应用于处理用户行为序列、上下文特征等多模态数据的统一建模，类似于VLM处理异构数据的思路，能够更好地捕捉用户意图和场景复杂性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:30:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06754v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06754v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Comprehensive visual, geometric, and semantic understanding of a 3D scene is crucial for successful execution of robotic tasks, especially in unstructured and complex environments. Additionally, to make robust decisions, it is necessary for the robot to evaluate the reliability of perceived information. While recent advances in 3D neural feature fields have enabled robots to leverage features from pretrained foundation models for tasks such as language-guided manipulation and navigation, existing methods suffer from two critical limitations: (i) they are typically scene-specific, and (ii) they lack the ability to model uncertainty in their predictions. We present UniFField, a unified uncertainty-aware neural feature field that combines visual, semantic, and geometric features in a single generalizable representation while also predicting uncertainty in each modality. Our approach, which can be applied zero shot to any new environment, incrementally integrates RGB-D images into our voxel-based feature representation as the robot explores the scene, simultaneously updating uncertainty estimation. We evaluate our uncertainty estimations to accurately describe the model prediction errors in scene reconstruction and semantic feature prediction. Furthermore, we successfully leverage our feature predictions and their respective uncertainty for an active object search task using a mobile manipulator robot, demonstrating the capability for robust decision-making.
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            <a href="https://www.alphaxiv.org/abs/2510.07105v1" target="_blank" rel="noopener noreferrer">
                Opt-ICL在LeWiDi-2025：通过元学习最大化来自评分者示例的上下文信号
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            <i class="fa fa-star mr-1"></i>6/10
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            Opt-ICL at LeWiDi-2025: Maximizing In-Context Signal from Rater Examples via Meta-Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taylor Sorensen, Yejin Choi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及上下文学习（ICL）优化和元学习，属于'使能LLM技术'范畴，因为改进的ICL方法可以增强推荐和搜索系统中基于示例的个性化建模。通过元学习最大化评分者信号的ICL优化，可以应用于搜索相关性评估和推荐系统用户偏好建模，提高系统对用户反馈的适应能力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:59:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07105v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07105v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Many natural language processing (NLP) tasks involve subjectivity, ambiguity, or legitimate disagreement between annotators. In this paper, we outline our system for modeling human variation. Our system leverages language models' (LLMs) in-context learning abilities, along with a two-step meta-learning training procedure for 1) post-training on many datasets requiring in-context learning and 2) specializing the model via in-context meta-learning to the particular data distribution of interest. We also evaluate the performance of our system submission to the Learning With Disagreements (LeWiDi) competition, where it was the overall winner on both tasks. Additionally, we perform an ablation study to measure the importance of each system component. We find that including rater examples in-context is crucial for our system's performance, dataset-specific fine-tuning is helpful on the larger datasets, post-training on other in-context datasets is helpful on one of the competition datasets, and that performance improves with model scale.
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            <a href="https://www.alphaxiv.org/abs/2510.06915v1" target="_blank" rel="noopener noreferrer">
                LongRM：揭示并突破奖励建模的上下文边界
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            <i class="fa fa-star mr-1"></i>6/10
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            LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang, Xiaobo Liang, Juntao Li, Mi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及奖励建模的上下文边界扩展，属于LLM核心技术进展。在推荐系统和搜索领域，更长的上下文边界可以显著提升用户行为序列建模能力，支持更复杂的用户历史交互分析，从而改进个性化推荐和搜索结果的质量。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:48:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06915v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06915v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largely neglecting the critical dimension of long context-response consistency. In this work, we introduce Long-RewardBench, a benchmark specifically designed for long-context RM evaluation, featuring both Pairwise Comparison and Best-of-N tasks. Our preliminary study reveals that even state-of-the-art generative RMs exhibit significant fragility in long-context scenarios, failing to maintain context-aware preference judgments. Motivated by the analysis of failure patterns observed in model outputs, we propose a general multi-stage training strategy that effectively scales arbitrary models into robust Long-context RMs (LongRMs). Experiments show that our approach not only substantially improves performance on long-context evaluation but also preserves strong short-context capability. Notably, our 8B LongRM outperforms much larger 70B-scale baselines and matches the performance of the proprietary Gemini 2.5 Pro model.
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            <a href="https://www.alphaxiv.org/abs/2510.06780v1" target="_blank" rel="noopener noreferrer">
                大语言模型知识物化的基础：终止性、可复现性与鲁棒性
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            Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luca Giordano, Simon Razniewski
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM知识物化的基础理论问题，属于'核心LLM技术进展'范畴。虽然不直接涉及推荐/搜索/广告应用，但知识物化的终止性、可复现性和鲁棒性对于构建可靠的LLM推荐系统至关重要，可确保推荐结果的一致性和稳定性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:03:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06780v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06780v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large Language Models (LLMs) encode substantial factual knowledge, yet measuring and systematizing this knowledge remains challenging. Converting it into structured format, for example through recursive extraction approaches such as the GPTKB methodology (Hu et al., 2025b), is still underexplored. Key open questions include whether such extraction can terminate, whether its outputs are reproducible, and how robust they are to variations. We systematically study LLM knowledge materialization using miniGPTKBs (domain-specific, tractable subcrawls), analyzing termination, reproducibility, and robustness across three categories of metrics: yield, lexical similarity, and semantic similarity. We experiment with four variations (seed, language, randomness, model) and three illustrative domains (from history, entertainment, and finance). Our findings show (i) high termination rates, though model-dependent; (ii) mixed reproducibility; and (iii) robustness that varies by perturbation type: high for seeds and temperature, lower for languages and models. These results suggest that LLM knowledge materialization can reliably surface core knowledge, while also revealing important limitations.
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            <a href="https://www.alphaxiv.org/abs/2510.06670v1" target="_blank" rel="noopener noreferrer">
                PIKA：从头开始用于后训练对齐的专家级合成数据集
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            PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi, Hongzhi Li,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及LLM后训练对齐的合成数据生成技术，属于'Enabling LLM Tech'范畴。在推荐系统、搜索和广告领域，高质量的对齐数据对于构建更安全、更符合业务目标的模型至关重要，特别是在处理用户敏感数据和商业约束时。然而，该方法更偏向通用LLM对齐而非特定领域应用，因此相关性中等。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:47:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06670v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06670v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone for aligning large language models (LLMs). However, its effectiveness depends on high-quality instruction data. Most existing alignment datasets are either private or require costly human annotation, which limits reproducibility and scalability. Even with Reinforcement Learning from AI Feedback (RLAIF), concerns about data quality remain. Moreover, it is unclear how much data is actually required to fine-tune a base model into a strong instruction-following model. Current approaches often rely on over 300k examples even at the supervised fine-tuning (SFT) stage, yet they still underperform compared to proprietary models, creating barriers for academic and resource-limited communities. To address this gap, we introduce PiKa, a data-efficient family of expert-level alignment datasets. In particular, the PiKa-SFT dataset uses only 30k SFT examples, far fewer than state-of-the-art datasets like Magpie. Through evaluations by fine-tuning Llama-3-8B-Base on PiKa and other public datasets, we show that PiKa-SFT outperforms models trained on much larger data. On AlpacaEval 2.0 and Arena-Hard benchmarks, PiKa-SFT fine-tuning even surpasses the official Llama-3-8B-Instruct model trained on over 10 million proprietary examples. We further extend our study by training the Qwen2.5 series (0.5B to 7B) on PiKa-SFT, achieving consistent gains. These findings demonstrate that high-quality alignment can be achieved with significantly less data, offering a scalable path for open-source LLM alignment. Code and data: https://github.com/SJY8460/PiKa.
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            <a href="https://www.alphaxiv.org/abs/2510.06843v1" target="_blank" rel="noopener noreferrer">
                SID：基于自我信号驱动的多LLM辩论
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            <i class="fa fa-star mr-1"></i>3/10
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            SID: Multi-LLM Debate Driven by Self Signals
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuhang Chen, Zhifan Song, Deyi Ji, Shuo Gao, Lanyun Zhu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多LLM辩论机制，属于LLM推理技术范畴，但未明确说明其在推荐系统、搜索或广告中的具体应用。虽然多模型协作可能用于提升内容理解或决策质量，但缺乏直接的应用场景说明，因此相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:10:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06843v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06843v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have exhibited impressive capabilities across diverse application domains. Recent work has explored Multi-LLM Agent Debate (MAD) as a way to enhance performance by enabling multiple LLMs to discuss and refine responses iteratively. Nevertheless, existing MAD methods predominantly focus on utilizing external structures, such as debate graphs, using LLM-as-a-Judge, while neglecting the application of self signals, such as token logits and attention, that arise during generation. This omission leads to redundant computation and potential performance degradation. In this paper, we shift the focus to the self signals of multi-LLM debate and introduce a Self-Signals Driven Multi-LLM Debate (SID), which leverages two types of self-signals: model-level confidence and token-level semantic focus, to adaptively guide the debate process. Our approach enables high-confidence agents to exit early at the model level and compress the redundant debate contents based on the attention mechanism. We evaluate our method on various LLMs and Multimodal LLMs across multiple challenging benchmarks. Experimental results demonstrate that our method not only outperforms existing MAD techniques in accuracy but also reduces token consumption, highlighting the effectiveness of utilizing self signals in enhancing both the performance and efficiency of multi-agent debate systems. Our code will be available at~\href{https://github.com/xuhang2019/SID}{\texttt{https://github.com/xuhang2019/SID}}.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06730v1" target="_blank" rel="noopener noreferrer">
                PTEB：基于大语言模型在评估时随机复述的鲁棒文本嵌入评估方法
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manuel Frank, Haithem Afli
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本嵌入评估方法，属于LLM评估领域，与您关注的推荐系统、搜索或广告核心进展相关性较弱。虽然文本嵌入技术可能间接应用于搜索中的语义匹配，但论文重点在于评估方法本身而非直接的应用创新，因此评分较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:37:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06730v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06730v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based method grounded in semantic textual similarity gold ratings, we show that LLMs generate token-diverse but semantically preserving, paraphrases. Across 7 MTEB tasks, we validate our hypothesis that the performance of sentence encoders is sensitive to changes in token space even when semantics remain fixed. We also observe that smaller models are not disproportionately affected relative to larger ones. Our results are statistically robust over multiple runs and we extended our experiments to 3 multilingual datasets covering 10 languages. More generally, we aim to propose a new evaluation paradigm in NLP that relies less on static, pre-defined benchmarks but shifts towards dynamic, stochastic evaluation leveraging eval-time compute.
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            <a href="https://www.alphaxiv.org/abs/2510.07115v1" target="_blank" rel="noopener noreferrer">
                增强基于CLIP的概念瓶颈模型中的概念定位能力
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier, Goran Frehse, Gianni Franchi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注CLIP模型中的概念定位改进，属于计算机视觉领域的技术优化。虽然CLIP本身是视觉-语言模型，但论文焦点是概念定位而非异构数据统一建模，与VLM类比异构数据的相关性较弱。在推荐系统、搜索或广告中，这种概念定位能力的潜在应用场景有限，主要是视觉内容理解而非核心排序或推荐任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:07:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07115v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07115v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper addresses explainable AI (XAI) through the lens of Concept Bottleneck Models (CBMs) that do not require explicit concept annotations, relying instead on concepts extracted using CLIP in a zero-shot manner. We show that CLIP, which is central in these techniques, is prone to concept hallucination, incorrectly predicting the presence or absence of concepts within an image in scenarios used in numerous CBMs, hence undermining the faithfulness of explanations. To mitigate this issue, we introduce Concept Hallucination Inhibition via Localized Interpretability (CHILI), a technique that disentangles image embeddings and localizes pixels corresponding to target concepts. Furthermore, our approach supports the generation of saliency-based explanations that are more interpretable.
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            <a href="https://www.alphaxiv.org/abs/2510.06855v1" target="_blank" rel="noopener noreferrer">
                在线通用事件边界检测
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Online Generic Event Boundary Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hyungrok Jung, Daneul Kim, Seunggyun Lim, Jeany Son, Jonghyun Choi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及事件边界检测，可能应用于理解用户行为序列中的模式变化。在推荐系统中，可用于检测用户兴趣转移点或会话边界，从而改进时序建模和个性化推荐。然而，作为通用事件检测而非专门针对推荐/搜索/广告场景，其直接相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:23:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06855v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06855v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
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                    Generic Event Boundary Detection (GEBD) aims to interpret long-form videos through the lens of human perception. However, current GEBD methods require processing complete video frames to make predictions, unlike humans processing data online and in real-time. To bridge this gap, we introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries of generic events immediately in streaming videos. This task faces unique challenges of identifying subtle, taxonomy-free event changes in real-time, without the access to future frames. To tackle these challenges, we propose a novel On-GEBD framework, Estimator, inspired by Event Segmentation Theory (EST) which explains how humans segment ongoing activity into events by leveraging the discrepancies between predicted and actual information. Our framework consists of two key components: the Consistent Event Anticipator (CEA), and the Online Boundary Discriminator (OBD). Specifically, the CEA generates a prediction of the future frame reflecting current event dynamics based solely on prior frames. Then, the OBD measures the prediction error and adaptively adjusts the threshold using statistical tests on past errors to capture diverse, subtle event transitions. Experimental results demonstrate that Estimator outperforms all baselines adapted from recent online video understanding models and achieves performance comparable to prior offline-GEBD methods on the Kinetics-GEBD and TAPOS datasets.
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            <a href="https://www.alphaxiv.org/abs/2510.06757v1" target="_blank" rel="noopener noreferrer">
                通过直方图匹配变换噪声分布：迈向适用于所有噪声的单一去噪器
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Transforming Noise Distributions with Histogram Matching: Towards a Single Denoiser for All
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sheng Fu, Junchao Zhang, Kailun Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注图像去噪领域的通用方法，属于计算机视觉技术。虽然去噪技术可能间接应用于推荐或搜索系统中的数据预处理，但论文本身没有明确提及在推荐系统、搜索或广告领域的应用潜力。该技术主要针对视觉数据而非推荐系统常见的异构数据模态。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:34:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06757v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06757v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Supervised Gaussian denoisers exhibit limited generalization when confronted with out-of-distribution noise, due to the diverse distributional characteristics of different noise types. To bridge this gap, we propose a histogram matching approach that transforms arbitrary noise towards a target Gaussian distribution with known intensity. Moreover, a mutually reinforcing cycle is established between noise transformation and subsequent denoising. This cycle progressively refines the noise to be converted, making it approximate the real noise, thereby enhancing the noise transformation effect and further improving the denoising performance. We tackle specific noise complexities: local histogram matching handles signal-dependent noise, intrapatch permutation processes channel-related noise, and frequency-domain histogram matching coupled with pixel-shuffle down-sampling breaks spatial correlation. By applying these transformations, a single Gaussian denoiser gains remarkable capability to handle various out-of-distribution noises, including synthetic noises such as Poisson, salt-and-pepper and repeating pattern noises, as well as complex real-world noises. Extensive experiments demonstrate the superior generalization and effectiveness of our method.
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            <a href="https://www.alphaxiv.org/abs/2510.06987v1" target="_blank" rel="noopener noreferrer">
                面向数据科学与机器学习生命周期的螺旋模型技术
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Spiral Model Technique For Data Science & Machine Learning Lifecycle
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rohith Mahadevan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文讨论的是通用的数据科学和机器学习生命周期管理方法，属于通用的MLOps或开发流程范畴。虽然机器学习生命周期管理在推荐系统、搜索或广告中有应用，但这属于通用的工程实践，而非核心领域进展、LLM技术、Transformer架构或直接应用。该主题更偏向于通用的机器学习工程流程，与当前聚焦的技术创新方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:11:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06987v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06987v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.SE</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is mostly depicted that it is possible in a traditional data science life cycle to start the process again after reaching the end of cycle. This paper suggests a new technique to incorporate data science life cycle to business problems that have a clear end goal. A new technique called spiral technique is introduced to emphasize versatility, agility and iterative approach to business processes.
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            <a href="https://www.alphaxiv.org/abs/2510.06838v1" target="_blank" rel="noopener noreferrer">
                无标签跨域：用于术语提取的远程监督
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            Crossing Domains without Labels: Distant Supervision for Term Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Elena Senger, Yuri Campbell, Rob van der Goot, Barbara Plank
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注术语提取和跨域迁移学习，这属于通用NLP技术而非专门针对推荐系统、搜索或广告领域。虽然远程监督技术可能在某些文本处理场景中有间接应用，但论文没有明确展示在RecSys/Search/Ads中的直接应用潜力，且不属于核心领域进展或关键的Transformer/LLM使能技术。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:02:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06838v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06838v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evaluation at both the document- and corpus-levels. Furthermore, we propose a robust LLM-based model that outperforms both supervised cross-domain encoder models and few-shot learning baselines and performs competitively with its GPT-4o teacher on this benchmark. The first step of our approach is generating psuedo-labels with this black-box LLM on general and scientific domains to ensure generalizability. Building on this data, we fine-tune the first LLMs for ATE. To further enhance document-level consistency, oftentimes needed for downstream tasks, we introduce lightweight post-hoc heuristics. Our approach exceeds previous approaches on 5/7 domains with an average improvement of 10 percentage points. We release our dataset and fine-tuned models to support future research in this area.
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            <a href="https://www.alphaxiv.org/abs/2510.06658v1" target="_blank" rel="noopener noreferrer">
                我们能否将机器隐藏在人群中？量化LLM参与循环标注任务中的等价性
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            <i class="fa fa-star mr-1"></i>2/10
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            Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaman He, Zikang Leng, Dana McKay, Damiano Spina, Johanne R. Trippas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在标注任务中的等价性评估，属于数据标注和评估方法范畴。虽然涉及LLM技术，但主要聚焦于标注任务本身而非在推荐系统、搜索或广告中的直接应用。论文内容更偏向于数据工程和评估方法，与当前关注的四大核心方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:17:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06658v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06658v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals. Rather than treating LLMs purely as annotation systems, we approach LLMs as an alternative annotation mechanism that may be capable of mimicking the subjective judgments made by humans. To assess this, we develop a statistical evaluation method based on Krippendorff's $\alpha$, paired bootstrapping, and the Two One-Sided t-Tests (TOST) equivalence test procedure. This evaluation method tests whether an LLM can blend into a group of human annotators without being distinguishable. We apply this approach to two datasets -- MovieLens 100K and PolitiFact -- and find that the LLM is statistically indistinguishable from a human annotator in the former ($p = 0.004$), but not in the latter ($p = 0.155$), highlighting task-dependent differences. It also enables early evaluation on a small sample of human data to inform whether LLMs are suitable for large-scale annotation in a given application.
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            <a href="https://www.alphaxiv.org/abs/2510.07309v1" target="_blank" rel="noopener noreferrer">
                Agent Bain vs. Agent McKinsey：面向商业领域的新型文本到SQL基准测试
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            Agent Bain vs. Agent McKinsey: A New Text-to-SQL Benchmark for the Business Domain
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yue Li, Ran Tao, Derek Hommel, Yusuf Denizay Dönder, Sungyong Chang, David Mimno...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到SQL转换的基准测试，属于数据库查询领域的特定应用。虽然文本到SQL技术理论上可以用于搜索系统中的自然语言查询处理，但论文聚焦于商业咨询领域的基准比较，与推荐系统、搜索排序或广告核心技术的直接关联性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:57:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07309v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07309v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In the business domain, where data-driven decision making is crucial, text-to-SQL is fundamental for easy natural language access to structured data. While recent LLMs have achieved strong performance in code generation, existing text-to-SQL benchmarks remain focused on factual retrieval of past records. We introduce CORGI, a new benchmark specifically designed for real-world business contexts. CORGI is composed of synthetic databases inspired by enterprises such as Doordash, Airbnb, and Lululemon. It provides questions across four increasingly complex categories of business queries: descriptive, explanatory, predictive, and recommendational. This challenge calls for causal reasoning, temporal forecasting, and strategic recommendation, reflecting multi-level and multi-step agentic intelligence. We find that LLM performance drops on high-level questions, struggling to make accurate predictions and offer actionable plans. Based on execution success rate, the CORGI benchmark is about 21\% more difficult than the BIRD benchmark. This highlights the gap between popular LLMs and the need for real-world business intelligence. We release a public dataset and evaluation framework, and a website for public submissions.
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            <a href="https://www.alphaxiv.org/abs/2510.07300v1" target="_blank" rel="noopener noreferrer">
                原生思考：通过一致性增强的强化学习解锁多语言推理能力
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            Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多语言推理和强化学习的一致性增强，属于纯粹的NLP技术范畴。虽然强化学习在推荐系统中可能有应用，但论文标题明确聚焦于多语言推理，这与RecSys/Search/Ads的核心需求关联度很低，且没有明确展示在推荐领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:55:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07300v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07300v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the "think-then-answer" paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations when processing non-English languages: (1) They often struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. These limitations significantly degrade the user experience for non-English speakers and hinder the global deployment of LRMs. To address these limitations, we propose M-Thinker, which is trained by the GRPO algorithm that involves a Language Consistency (LC) reward and a novel Cross-lingual Thinking Alignment (CTA) reward. Specifically, the LC reward defines a strict constraint on the language consistency between the input, thought, and answer. Besides, the CTA reward compares the model's non-English reasoning paths with its English reasoning path to transfer its own reasoning capability from English to non-English languages. Through an iterative RL procedure, our M-Thinker-1.5B/7B models not only achieve nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath), but also exhibit excellent generalization on out-of-domain languages.
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            <a href="https://www.alphaxiv.org/abs/2510.07284v1" target="_blank" rel="noopener noreferrer">
                基于成对比较的在线评分标准获取
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            <i class="fa fa-star mr-1"></i>2/10
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            Online Rubrics Elicitation from Pairwise Comparisons
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>MohammadHossein Rezaei, Robert Vacareanu, Zihao Wang, Clinton Wang, Yunzhong He,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注从成对比较中获取评分标准，这属于偏好学习的一般方法，与推荐系统中的用户偏好建模有一定关联。然而，该方法缺乏与LLM技术、Transformer架构或异构数据统一建模的具体联系，在搜索、推荐或广告领域的直接应用潜力有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:44:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07284v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07284v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Rubrics provide a flexible way to train LLMs on open-ended long-form answers where verifiable rewards are not applicable and human preferences provide coarse signals. Prior work shows that reinforcement learning with rubric-based rewards leads to consistent gains in LLM post-training. Most existing approaches rely on rubrics that remain static over the course of training. Such static rubrics, however, are vulnerable to reward-hacking type behaviors and fail to capture emergent desiderata that arise during training. We introduce Online Rubrics Elicitation (OnlineRubrics), a method that dynamically curates evaluation criteria in an online manner through pairwise comparisons of responses from current and reference policies. This online process enables continuous identification and mitigation of errors as training proceeds. Empirically, this approach yields consistent improvements of up to 8% over training exclusively with static rubrics across AlpacaEval, GPQA, ArenaHard as well as the validation sets of expert questions and rubrics. We qualitatively analyze the elicited criteria and identify prominent themes such as transparency, practicality, organization, and reasoning.
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            <a href="https://www.alphaxiv.org/abs/2510.07242v1" target="_blank" rel="noopener noreferrer">
                混合强化学习：当奖励稀疏时，采用密集策略更优
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            Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leitian Tao, Ilia Kulikov, Swarnadeep Saha, Tianlu Wang, Jing Xu, Yixuan Li, Jas...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注强化学习中奖励稀疏性的通用技术问题，属于纯粹的强化学习研究。虽然强化学习在推荐系统中有所应用，但论文标题并未表明与推荐、搜索或广告系统的具体关联，也没有提及LLM、Transformer或异质数据建模等当前关注的技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:09:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07242v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07242v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Post-training for reasoning of large language models (LLMs) increasingly relies on verifiable rewards: deterministic checkers that provide 0-1 correctness signals. While reliable, such binary feedback is brittle--many tasks admit partially correct or alternative answers that verifiers under-credit, and the resulting all-or-nothing supervision limits learning. Reward models offer richer, continuous feedback, which can serve as a complementary supervisory signal to verifiers. We introduce HERO (Hybrid Ensemble Reward Optimization), a reinforcement learning framework that integrates verifier signals with reward-model scores in a structured way. HERO employs stratified normalization to bound reward-model scores within verifier-defined groups, preserving correctness while refining quality distinctions, and variance-aware weighting to emphasize challenging prompts where dense signals matter most. Across diverse mathematical reasoning benchmarks, HERO consistently outperforms RM-only and verifier-only baselines, with strong gains on both verifiable and hard-to-verify tasks. Our results show that hybrid reward design retains the stability of verifiers while leveraging the nuance of reward models to advance reasoning.
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            <a href="https://www.alphaxiv.org/abs/2510.07239v1" target="_blank" rel="noopener noreferrer">
                红队-强盗：通过强盗引导的LoRA专家进行LLM红队测试的测试时自适应
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        <div class="mb-2 text-base text-gray-700">
            Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christos Ziakas, Nicholas Loo, Nishita Jain, Alessandra Russo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM红队测试（安全测试）和测试时自适应，这属于安全评估领域，被明确列为不相关主题。虽然提到了LoRA（低秩自适应）技术，但其应用场景是红队测试而非推荐系统、搜索或广告中的实际应用。该工作没有展示在推荐、搜索或广告领域的具体应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:06:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07239v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07239v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack mechanisms to efficiently adapt to model-specific vulnerabilities at inference. We introduce Red-Bandit, a red-teaming framework that adapts online to identify and exploit model failure modes under distinct attack styles (e.g., manipulation, slang). Red-Bandit post-trains a set of parameter-efficient LoRA experts, each specialized for a particular attack style, using reinforcement learning that rewards the generation of unsafe prompts via a rule-based safety model. At inference, a multi-armed bandit policy dynamically selects among these attack-style experts based on the target model's response safety, balancing exploration and exploitation. Red-Bandit achieves state-of-the-art results on AdvBench under sufficient exploration (ASR@10), while producing more human-readable prompts (lower perplexity). Moreover, Red-Bandit's bandit policy serves as a diagnostic tool for uncovering model-specific vulnerabilities by indicating which attack styles most effectively elicit unsafe behaviors.
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            <a href="https://www.alphaxiv.org/abs/2510.07233v1" target="_blank" rel="noopener noreferrer">
                LAD-RAG：面向视觉丰富文档理解的布局感知动态检索增强生成
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeet...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉丰富文档理解，属于计算机视觉与文档处理的交叉领域。虽然RAG技术在搜索系统中具有应用潜力，但该论文明确聚焦于视觉文档布局理解，与推荐系统、搜索或广告的核心技术关联较弱，且未明确涉及异构数据统一建模或Transformer架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:02:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07233v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07233v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents' structural organization and cross-page dependencies. However, conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference, regardless of the specific demands of the question or context. This often results in incomplete evidence retrieval and degraded answer quality for multi-page reasoning tasks. To address these limitations, we propose LAD-RAG, a novel Layout-Aware Dynamic RAG framework. During ingestion, LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies, adding it alongside standard neural embeddings to yield a more holistic representation of the document. During inference, an LLM agent dynamically interacts with the neural and symbolic indices to adaptively retrieve the necessary evidence based on the query. Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels, yielding higher QA accuracy with minimal latency.
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            <a href="https://www.alphaxiv.org/abs/2510.07231v1" target="_blank" rel="noopener noreferrer">
                基于科学验证关系的大语言模型因果推理能力基准测试
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            Benchmarking LLM Causal Reasoning with Scientifically Validated Relationships
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Donggyu Lee, Sungwon Park, Yerin Hwang, Hyunwoo Oh, Hyoshin Kim, Jungwon Kim, Me...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM因果推理能力的基准测试和评估，属于纯粹的NLP评估基准主题，这在无关主题中被明确排除。虽然因果推理在推荐系统中有潜在应用，但该论文的重点是基准测试而非实际应用，因此与当前关注点相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:00:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07231v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07231v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Causal reasoning is fundamental for Large Language Models (LLMs) to understand genuine cause-and-effect relationships beyond pattern matching. Existing benchmarks suffer from critical limitations such as reliance on synthetic data and narrow domain coverage. We introduce a novel benchmark constructed from casually identified relationships extracted from top-tier economics and finance journals, drawing on rigorous methodologies including instrumental variables, difference-in-differences, and regression discontinuity designs. Our benchmark comprises 40,379 evaluation items covering five task types across domains such as health, environment, technology, law, and culture. Experimental results on eight state-of-the-art LLMs reveal substantial limitations, with the best model achieving only 57.6\% accuracy. Moreover, model scale does not consistently translate to superior performance, and even advanced reasoning models struggle with fundamental causal relationship identification. These findings underscore a critical gap between current LLM capabilities and demands of reliable causal reasoning in high-stakes applications.
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            <a href="https://www.alphaxiv.org/abs/2510.07226v1" target="_blank" rel="noopener noreferrer">
                群体中的机器？衡量机器生成文本在Reddit上的足迹
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            Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lucio La Cava, Luca Maria Aiello, Andrea Tagarelli
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注机器生成文本的检测和测量，这属于内容生成和检测领域，而非推荐系统、搜索或广告的核心技术。虽然可能涉及一些文本分析技术，但与我的关注点（如核心推荐算法、Transformer架构改进、LLM在推荐中的直接应用等）关联度很低，且更偏向内容安全而非技术架构创新。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:57:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07226v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07226v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span><span class="category-tag">physics.soc-ph</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generative Artificial Intelligence is reshaping online communication by enabling large-scale production of Machine-Generated Text (MGT) at low cost. While its presence is rapidly growing across the Web, little is known about how MGT integrates into social media environments. In this paper, we present the first large-scale characterization of MGT on Reddit. Using a state-of-the-art statistical method for detection of MGT, we analyze over two years of activity (2022-2024) across 51 subreddits representative of Reddit's main community types such as information seeking, social support, and discussion. We study the concentration of MGT across communities and over time, and compared MGT to human-authored text in terms of social signals it expresses and engagement it receives. Our very conservative estimate of MGT prevalence indicates that synthetic text is marginally present on Reddit, but it can reach peaks of up to 9% in some communities in some months. MGT is unevenly distributed across communities, more prevalent in subreddits focused on technical knowledge and social support, and often concentrated in the activity of a small fraction of users. MGT also conveys distinct social signals of warmth and status giving typical of language of AI assistants. Despite these stylistic differences, MGT achieves engagement levels comparable than human-authored content and in a few cases even higher, suggesting that AI-generated text is becoming an organic component of online social discourse. This work offers the first perspective on the MGT footprint on Reddit, paving the way for new investigations involving platform governance, detection strategies, and community dynamics.
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            <a href="https://www.alphaxiv.org/abs/2510.07203v1" target="_blank" rel="noopener noreferrer">
                向日葵：一种扩展大型语言模型中非洲语言覆盖范围的新方法
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        <div class="mb-2 text-base text-gray-700">
            Sunflower: A New Approach To Expanding Coverage of African Languages in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Benjamin Akera, Evelyn Nafula Ouma, Gilbert Yiga, Patrick Walukagga, Phionah Nat...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多语言能力扩展，属于LLM技术的基础能力建设。虽然多语言支持对于全球化的推荐和搜索系统有一定价值，但这属于通用的LLM能力改进，而非专门针对推荐系统、搜索或广告领域的核心技术创新。论文焦点是语言覆盖扩展，与当前关注的推荐系统架构、Transformer效率改进或异构数据建模等核心技术方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:35:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07203v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07203v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    There are more than 2000 living languages in Africa, most of which have been bypassed by advances in language technology. Current leading LLMs exhibit strong performance on a number of the most common languages (e.g. Swahili or Yoruba), but prioritise support for the languages with the most speakers first, resulting in piecemeal ability across disparate languages. We contend that a regionally focussed approach is more efficient, and present a case study for Uganda, a country with high linguistic diversity. We describe the development of Sunflower 14B and 32B, a pair of models based on Qwen 3 with state of the art comprehension in the majority of all Ugandan languages. These models are open source and can be used to reduce language barriers in a number of important practical applications.
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            <a href="https://www.alphaxiv.org/abs/2510.07178v1" target="_blank" rel="noopener noreferrer">
                无偏见语言模型学习非自然模式：大型语言模型如何无法区分可能与不可能
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            Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Imry Ziv, Nur Lan, Emmanuel Chemla, Roni Katzir
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的偏见问题和自然语言理解能力缺陷，属于纯粹的NLP评估和基准测试范畴。虽然涉及语言模型，但焦点是模型的内在认知能力评估，而非在推荐系统、搜索或广告中的实际应用或架构改进，与我的技术应用导向不符。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:17:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07178v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07178v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Are large language models (LLMs) sensitive to the distinction between humanly possible languages and humanly impossible languages? This question is taken by many to bear on whether LLMs and humans share the same innate learning biases. Previous work has attempted to answer it in the positive by comparing LLM learning curves on existing language datasets and on "impossible" datasets derived from them via various perturbation functions. Using the same methodology, we examine this claim on a wider set of languages and impossible perturbations. We find that in most cases, GPT-2 learns each language and its impossible counterpart equally easily, in contrast to previous claims. We also apply a more lenient condition by testing whether GPT-2 provides any kind of separation between the whole set of natural languages and the whole set of impossible languages. By considering cross-linguistic variance in various metrics computed on the perplexity curves, we show that GPT-2 provides no systematic separation between the possible and the impossible. Taken together, these perspectives show that LLMs do not share the human innate biases that shape linguistic typology.
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            <a href="https://www.alphaxiv.org/abs/2510.07177v1" target="_blank" rel="noopener noreferrer">
                CARPAS：面向大语言模型摘要任务中提供方面的内容感知细化
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yong-En Tian, Yu-Chien Tang, An-Zi Yen, Wen-Chih Peng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注大语言模型在摘要生成任务中的内容感知细化技术，属于纯粹的LLM应用范畴。虽然摘要技术在某些搜索场景中可能有间接应用，但论文本身没有明确展示在推荐系统、搜索或广告中的直接应用潜力，且更侧重于内容生成而非核心排名或检索任务。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:16:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07177v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07177v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Aspect-based summarization has attracted significant attention for its ability to generate more fine-grained and user-aligned summaries. While most existing approaches assume a set of predefined aspects as input, real-world scenarios often present challenges where these given aspects may be incomplete, irrelevant, or entirely missing from the document. Users frequently expect systems to adaptively refine or filter the provided aspects based on the actual content. In this paper, we initiate this novel task setting, termed Content-Aware Refinement of Provided Aspects for Summarization (CARPAS), with the aim of dynamically adjusting the provided aspects based on the document context before summarizing. We construct three new datasets to facilitate our pilot experiments, and by using LLMs with four representative prompting strategies in this task, we find that LLMs tend to predict an overly comprehensive set of aspects, which often results in excessively long and misaligned summaries. Building on this observation, we propose a preliminary subtask to predict the number of relevant aspects, and demonstrate that the predicted number can serve as effective guidance for the LLMs, reducing the inference difficulty, and enabling them to focus on the most pertinent aspects. Our extensive experiments show that the proposed approach significantly improves performance across all datasets. Moreover, our deeper analyses uncover LLMs' compliance when the requested number of aspects differs from their own estimations, establishing a crucial insight for the deployment of LLMs in similar real-world applications.
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            <a href="https://www.alphaxiv.org/abs/2510.07167v1" target="_blank" rel="noopener noreferrer">
                层次文本分类的推理机制：以专利文本为例
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Reasoning for Hierarchical Text Classification: The Case of Patents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lekang Jiang, Wenjun Sun, Stephan Goetz
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于专利领域的层次文本分类，这属于特定领域的文档分类任务，与推荐系统、搜索或广告的核心技术关联性较弱。虽然文本分类技术可能间接应用于内容理解，但论文的专利领域特定性和层次分类焦点使其在推荐/搜索/广告领域的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:06:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07167v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07167v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Hierarchical text classification (HTC) assigns documents to multiple levels of a pre-defined taxonomy. Automated patent subject classification represents one of the hardest HTC scenarios because of domain knowledge difficulty and a huge number of labels. Prior approaches only output a flat label set, which offers little insight into the reason behind predictions. Therefore, we propose Reasoning for Hierarchical Classification (RHC), a novel framework that reformulates HTC as a step-by-step reasoning task to sequentially deduce hierarchical labels. RHC trains large language models (LLMs) in two stages: a cold-start stage that aligns outputs with chain-of-thought (CoT) reasoning format and a reinforcement learning (RL) stage to enhance multi-step reasoning ability. RHC demonstrates four advantages in our experiments. (1) Effectiveness: RHC surpasses previous baselines and outperforms the supervised fine-tuning counterparts by approximately 3% in accuracy and macro F1. (2) Explainability: RHC produces natural-language justifications before prediction to facilitate human inspection. (3) Scalability: RHC scales favorably with model size with larger gains compared to standard fine-tuning. (4) Applicability: Beyond patents, we further demonstrate that RHC achieves state-of-the-art performance on other widely used HTC benchmarks, which highlights its broad applicability.
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            <a href="https://www.alphaxiv.org/abs/2510.07141v1" target="_blank" rel="noopener noreferrer">
                比较人类和语言模型在复杂结构上的句子处理困难
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Comparing human and language models sentence processing difficulties on complex structures
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Samuel Joseph Amouyal, Aya Meltzer-Asscher, Jonathan Berant
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注人类与语言模型在句子处理困难方面的比较，这属于纯粹的NLP评估基准研究。虽然涉及语言模型，但研究重点在于认知科学层面的处理困难比较，而非在搜索、推荐或广告领域的实际应用或架构改进。论文缺乏明确的实际应用场景或技术改进潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:42:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07141v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07141v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) that fluently converse with humans are a reality - but do LLMs experience human-like processing difficulties? We systematically compare human and LLM sentence comprehension across seven challenging linguistic structures. We collect sentence comprehension data from humans and five families of state-of-the-art LLMs, varying in size and training procedure in a unified experimental framework. Our results show LLMs overall struggle on the target structures, but especially on garden path (GP) sentences. Indeed, while the strongest models achieve near perfect accuracy on non-GP structures (93.7% for GPT-5), they struggle on GP structures (46.8% for GPT-5). Additionally, when ranking structures based on average performance, rank correlation between humans and models increases with parameter count. For each target structure, we also collect data for their matched baseline without the difficult structure. Comparing performance on the target vs. baseline sentences, the performance gap observed in humans holds for LLMs, with two exceptions: for models that are too weak performance is uniformly low across both sentence types, and for models that are too strong the performance is uniformly high. Together, these reveal convergence and divergence in human and LLM sentence comprehension, offering new insights into the similarity of humans and LLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.07098v1" target="_blank" rel="noopener noreferrer">
                TALENT：通过增强语言的自然文本转录实现表格视觉问答
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            TALENT: Table VQA via Augmented Language-Enhanced Natural-text Transcription
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guo Yutong, Wanying Wang, Yue Wu, Zichen Miao, Haoyu Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于表格视觉问答，属于视觉语言模型在特定视觉模态的应用。虽然涉及多模态建模，但其核心是表格理解而非推荐/搜索/广告系统中的异构数据处理，且没有明确展示在推荐/搜索/广告领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:56:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Table Visual Question Answering (Table VQA) is typically addressed by large vision-language models (VLMs). While such models can answer directly from images, they often miss fine-grained details unless scaled to very large sizes, which are computationally prohibitive, especially for mobile deployment. A lighter alternative is to have a small VLM perform OCR and then use a large language model (LLM) to reason over structured outputs such as Markdown tables. However, these representations are not naturally optimized for LLMs and still introduce substantial errors. We propose TALENT (Table VQA via Augmented Language-Enhanced Natural-text Transcription), a lightweight framework that leverages dual representations of tables. TALENT prompts a small VLM to produce both OCR text and natural language narration, then combines them with the question for reasoning by an LLM. This reframes Table VQA as an LLM-centric multimodal reasoning task, where the VLM serves as a perception-narration module rather than a monolithic solver. Additionally, we construct ReTabVQA, a more challenging Table VQA dataset requiring multi-step quantitative reasoning over table images. Experiments show that TALENT enables a small VLM-LLM combination to match or surpass a single large VLM at significantly lower computational cost on both public datasets and ReTabVQA.
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            <a href="https://www.alphaxiv.org/abs/2510.07096v1" target="_blank" rel="noopener noreferrer">
                让机器听起来讽刺：LLM增强与检索引导的讽刺语音合成
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        <div class="mb-2 text-base text-gray-700">
            Making Machines Sound Sarcastic: LLM-Enhanced and Retrieval-Guided Sarcastic Speech Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhu Li, Yuqing Zhang, Xiyuan Gao, Shekhar Nayak, Matt Coler
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语音合成中的讽刺语气生成，属于语音处理领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及LLM技术，但其应用方向是语音合成而非推荐、搜索或广告中的文本理解或内容排序，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:53:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07096v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07096v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span><span class="category-tag">eess.AS</span></div>
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                    Sarcasm is a subtle form of non-literal language that poses significant challenges for speech synthesis due to its reliance on nuanced semantic, contextual, and prosodic cues. While existing speech synthesis research has focused primarily on broad emotional categories, sarcasm remains largely unexplored. In this paper, we propose a Large Language Model (LLM)-enhanced Retrieval-Augmented framework for sarcasm-aware speech synthesis. Our approach combines (1) semantic embeddings from a LoRA-fine-tuned LLaMA 3, which capture pragmatic incongruity and discourse-level cues of sarcasm, and (2) prosodic exemplars retrieved via a Retrieval Augmented Generation (RAG) module, which provide expressive reference patterns of sarcastic delivery. Integrated within a VITS backbone, this dual conditioning enables more natural and contextually appropriate sarcastic speech. Experiments demonstrate that our method outperforms baselines in both objective measures and subjective evaluations, yielding improvements in speech naturalness, sarcastic expressivity, and downstream sarcasm detection.
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                认知带宽瓶颈：将长视野智能体从动作规划转向模式规划
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            The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Baixuan Xu, Tianshi Zheng, Zhaowei Wang, Hong Ting Tsang, Weiqi Wang, Tianqing F...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注智能体规划和认知架构，属于通用AI智能体领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然规划效率可能间接影响某些系统，但论文没有明确展示在推荐、搜索或广告领域的直接应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:47:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07091v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07091v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt planning with actions where a executable action list would be provided as reference. However, this action representation choice would be impractical when the environment action space is combinatorial exploded (e.g., open-ended real world). This naturally leads to a question: As environmental action space scales, what is the optimal action representation for long-horizon agents? In this paper, we systematically study the effectiveness of two different action representations. The first one is conventional planning with actions (PwA) which is predominantly adopted for its effectiveness on existing benchmarks. The other one is planning with schemas (PwS) which instantiate an action schema into action lists (e.g., "move [OBJ] to [OBJ]" -> "move apple to desk") to ensure concise action space and reliable scalability. This alternative is motivated by its alignment with human cognition and its compliance with environment-imposed action format restriction. We propose cognitive bandwidth perspective as a conceptual framework to qualitatively understand the differences between these two action representations and empirically observe a representation-choice inflection point between ALFWorld (~35 actions) and SciWorld (~500 actions), which serve as evidence of the need for scalable representations. We further conduct controlled experiments to study how the location of this inflection point interacts with different model capacities: stronger planning proficiency shifts the inflection rightward, whereas better schema instantiation shifts it leftward. Finally, noting the suboptimal performance of PwS agents, we provide an actionable guide for building more capable PwS agents for better scalable autonomy.
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            <a href="https://www.alphaxiv.org/abs/2510.07081v1" target="_blank" rel="noopener noreferrer">
                通过局部确定性传播加速扩散大语言模型推理
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Accelerating Diffusion LLM Inference via Local Determinism Propagation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fanheng Kong, Jingyuan Zhang, Yahui Liu, Zirui Wu, Yu Tian, Victoria W., Guorui ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型（Diffusion Models）的推理加速技术，属于生成式AI领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然提到了LLM，但重点在于扩散模型的优化方法，而非LLM在推荐/搜索/广告中的直接应用或核心架构改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:39:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07081v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07081v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Diffusion large language models (dLLMs) represent a significant advancement in text generation, offering parallel token decoding capabilities. However, existing open-source implementations suffer from quality-speed trade-offs that impede their practical deployment. Conservative sampling strategies typically decode only the most confident token per step to ensure quality (i.e., greedy decoding), at the cost of inference efficiency due to repeated redundant refinement iterations--a phenomenon we term delayed decoding. Through systematic analysis of dLLM decoding dynamics, we characterize this delayed decoding behavior and propose a training-free adaptive parallel decoding strategy, named LocalLeap, to address these inefficiencies. LocalLeap is built on two fundamental empirical principles: local determinism propagation centered on high-confidence anchors and progressive spatial consistency decay. By applying these principles, LocalLeap identifies anchors and performs localized relaxed parallel decoding within bounded neighborhoods, achieving substantial inference step reduction through early commitment of already-determined tokens without compromising output quality. Comprehensive evaluation on various benchmarks demonstrates that LocalLeap achieves 6.94$\times$ throughput improvements and reduces decoding steps to just 14.2\% of the original requirement, achieving these gains with negligible performance impact. The source codes are available at: https://github.com/friedrichor/LocalLeap.
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            <a href="https://www.alphaxiv.org/abs/2510.07074v1" target="_blank" rel="noopener noreferrer">
                LuxInstruct：用于卢森堡语的多语言指令微调数据集
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            LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fred Philippy, Laura Bernardy, Siwen Guo, Jacques Klein, Tegawendé F. Bissyandé
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注低资源语言的指令微调数据集构建，属于多语言NLP领域。虽然指令微调是LLM技术的一部分，但卢森堡语作为特定低资源语言，在推荐系统、搜索或广告中的实际应用潜力非常有限，且论文未涉及任何与推荐/搜索/广告相关的具体技术或应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:35:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07074v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07074v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts. However, low-resource languages such as Luxembourgish face severe limitations due to the lack of high-quality instruction datasets. Traditional reliance on machine translation often introduces semantic misalignment and cultural inaccuracies. In this work, we address these challenges by creating a cross-lingual instruction tuning dataset for Luxembourgish, without resorting to machine-generated translations into it. Instead, by leveraging aligned data from English, French, and German, we build a high-quality dataset that preserves linguistic and cultural nuances. We provide evidence that cross-lingual instruction tuning not only improves representational alignment across languages but also the model's generative capabilities in Luxembourgish. This highlights how cross-lingual data curation can avoid the common pitfalls of machine-translated data and directly benefit low-resource language development.
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            <a href="https://www.alphaxiv.org/abs/2510.07037v1" target="_blank" rel="noopener noreferrer">
                超越单语假设：大语言模型时代下的语码转换自然语言处理综述
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        <div class="mb-2 text-base text-gray-700">
            Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal, Mayank Singh
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语码转换（code-switching）这一特定NLP任务，属于语言处理领域的专项研究。虽然涉及大语言模型，但其核心应用场景是多语言混合文本处理，与推荐系统、搜索或广告的核心技术需求关联度较低。语码转换技术可能在多语言搜索中有边缘应用，但并非当前关注的核心领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:04:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07037v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07037v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Code-switching (CSW), the alternation of languages and scripts within a single utterance, remains a fundamental challenge for multiling ual NLP, even amidst the rapid advances of large language models (LLMs). Most LLMs still struggle with mixed-language inputs, limited CSW datasets, and evaluation biases, hindering deployment in multilingual societies. This survey provides the first comprehensive analysis of CSW-aware LLM research, reviewing \total{unique_references} studies spanning five research areas, 12 NLP tasks, 30+ datasets, and 80+ languages. We classify recent advances by architecture, training strategy, and evaluation methodology, outlining how LLMs have reshaped CSW modeling and what challenges persist. The paper concludes with a roadmap emphasizing the need for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. A curated collection of all resources is maintained at https://github.com/lingo-iitgn/awesome-code-mixing/.
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            <a href="https://www.alphaxiv.org/abs/2510.07024v1" target="_blank" rel="noopener noreferrer">
                挖掘心智：从1亿条信念中揭示前沿大语言模型的知识边界
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            Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM知识边界和信念分析，属于纯粹的LLM评估研究。虽然涉及前沿LLM技术，但缺乏明确的推荐系统、搜索或广告应用场景。这种知识分析可能对理解LLM能力边界有帮助，但无法直接应用于推荐、搜索或广告系统的核心任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:48:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07024v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07024v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LLMs are remarkable artifacts that have revolutionized a range of NLP and AI tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we take a deep tour into the factual knowledge (or beliefs) of a frontier LLM, based on GPTKB v1.5 (Hu et al., 2025a), a recursively elicited set of 100 million beliefs of one of the strongest currently available frontier LLMs, GPT-4.1. We find that the models' factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks. We also find that inconsistency, ambiguity and hallucinations are major issues, shedding light on future research opportunities concerning factual LLM knowledge.
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            <a href="https://www.alphaxiv.org/abs/2510.07000v1" target="_blank" rel="noopener noreferrer">
                Pragyaan：为印度语言设计和策划高质量文化后训练数据集
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            Pragyaan: Designing and Curating High-Quality Cultural Post-Training Datasets for Indian Languages
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Neel Prabhanjan Rachamalla, Aravind Konakalla, Gautam Rajeev, Ashish Kulkarni, C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于为印度语言创建文化特定的后训练数据集，这属于特定语言和文化的NLP数据工程工作。虽然多语言能力在理论上可以支持全球化推荐/搜索系统，但论文标题明确聚焦于印度语言和文化数据集创建，与核心推荐系统、搜索广告技术或Transformer架构进步的直接关联性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:23:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07000v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07000v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The effectiveness of Large Language Models (LLMs) depends heavily on the availability of high-quality post-training data, particularly instruction-tuning and preference-based examples. Existing open-source datasets, however, often lack multilingual coverage, cultural grounding, and suffer from task diversity gaps that are especially pronounced for Indian languages. We introduce a human-in-the-loop pipeline that combines translations with synthetic expansion to produce reliable and diverse Indic post-training data. Using this pipeline, we curate two datasets: Pragyaan-IT (22.5K) and Pragyaan-Align (100K) across 10 Indian languages covering 13 broad and 56 sub-categories, leveraging 57 diverse datasets. Our dataset protocol incorporates several often-overlooked dimensions and emphasize task diversity, multi-turn dialogue, instruction fidelity, safety alignment, and preservation of cultural nuance, providing a foundation for more inclusive and effective multilingual LLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.06994v1" target="_blank" rel="noopener noreferrer">
                RedTWIZ：通过自适应攻击规划的多样化大语言模型红队测试
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            RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Artur Horal, Daniel Pina, Henrique Paz, Iago Paulo, João Soares, Rafael Ferreira...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM安全性和对抗性测试（红队测试），这属于安全评估范畴，与我的核心关注点无关。虽然涉及LLM技术，但其应用方向是安全测试而非推荐系统、搜索或广告领域的实际应用，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:18:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06994v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06994v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models (LLMs) in AI-assisted software development. Our work is driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbreaks; (2) a diverse generative multi-turn attack suite, supporting compositional, realistic and goal-oriented jailbreak conversational strategies; and (3) a hierarchical attack planner, which adaptively plans, serializes, and triggers attacks tailored to specific LLM's vulnerabilities. Together, these contributions form a unified framework -- combining assessment, attack generation, and strategic planning -- to comprehensively evaluate and expose weaknesses in LLMs' robustness. Extensive evaluation is conducted to systematically assess and analyze the performance of the overall system and each component. Experimental results demonstrate that our multi-turn adversarial attack strategies can successfully lead state-of-the-art LLMs to produce unsafe generations, highlighting the pressing need for more research into enhancing LLM's robustness.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06953v1" target="_blank" rel="noopener noreferrer">
                重新审视LLM推理轨迹中的均匀信息密度假设
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minju Gwak, Guijin Son, Jaehyung Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLM推理过程中的信息密度分布假设，属于纯理论的语言模型内部机制分析。虽然涉及LLM技术，但缺乏明确的推荐系统、搜索或广告应用场景，且更偏向NLP基础理论研究而非实际应用导向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:37:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06953v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06953v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Uniform Information Density (UID) hypothesis suggests that effective communication maintains a stable flow of information. In this work, we revisit this principle in the context of large language model (LLM) reasoning traces, asking whether step-level uniformity reflects reasoning quality. To this end, we propose an entropy-based stepwise information density metric and introduce two complementary measures of uniformity, local and global uniformity scores. Across the experiments on six different reasoning benchmarks, we find that step-level uniformity not only provides a strong theoretical lens but also yields practical performance benefits; for example, selecting reasoning traces with more uniform information density at the step-level improves accuracy by 10-32\% relative gains over baselines at AIME2025. Our analysis further reveals that correct reasoning traces tend to avoid sharp information density spikes, while incorrect traces exhibit irregular information bursts. These results demonstrate that UID-inspired information density measures outperform alternative internal signals as predictors of reasoning quality. Results highlight the uniformity of the information density as a robust diagnostic and selection criterion for building more reliable and accurate reasoning systems.
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            <a href="https://www.alphaxiv.org/abs/2510.06917v1" target="_blank" rel="noopener noreferrer">
                SHANKS：面向口语语言模型的同步听觉与思考机制
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注口语语言模型的听觉处理能力，属于语音处理领域。虽然标题提及语言模型，但核心焦点是语音理解而非文本处理，与推荐系统、搜索或广告的文本/序列建模需求关联度较低。口语处理技术可能在某些特定语音搜索场景有潜在应用，但整体相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:48:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06917v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06917v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">eess.AS</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from interacting during the user's turn and can lead to high response latency while it waits to think. Consequently, thinking after receiving the full input is not suitable for speech-to-speech interaction, where real-time, low-latency exchange is important. We address this by noting that humans naturally "think while listening." In this paper, we propose SHANKS, a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to the user input. SHANKS streams the input speech in fixed-duration chunks and, as soon as a chunk is received, generates unspoken reasoning based on all previous speech and reasoning, while the user continues speaking. SHANKS uses this unspoken reasoning to decide whether to interrupt the user and to make tool calls to complete the task. We demonstrate that SHANKS enhances real-time user-SLM interaction in two scenarios: (1) when the user is presenting a step-by-step solution to a math problem, SHANKS can listen, reason, and interrupt when the user makes a mistake, achieving 37.1% higher interruption accuracy than a baseline that interrupts without thinking; and (2) in a tool-augmented dialogue, SHANKS can complete 56.9% of the tool calls before the user finishes their turn. Overall, SHANKS moves toward models that keep thinking throughout the conversation, not only after a turn ends. Animated illustrations of Shanks can be found at https://d223302.github.io/SHANKS/
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            <a href="https://www.alphaxiv.org/abs/2510.06889v1" target="_blank" rel="noopener noreferrer">
                MeXtract：从科学论文中进行轻量级元数据提取
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            MeXtract: Light-Weight Metadata Extraction from Scientific Papers
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zaid Alyafeai, Maged S. Al-Shaibani, Bernard Ghanem
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于科学论文的元数据提取，这是一个文档处理任务，与推荐系统、搜索或广告的核心领域进展没有直接关联。虽然元数据提取在文档搜索中有一些应用，但该论文的特定科学论文焦点和轻量级提取方法在推荐、搜索或广告领域缺乏明确的直接应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:12:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06889v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06889v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Metadata plays a critical role in indexing, documenting, and analyzing scientific literature, yet extracting it accurately and efficiently remains a challenging task. Traditional approaches often rely on rule-based or task-specific models, which struggle to generalize across domains and schema variations. In this paper, we present MeXtract, a family of lightweight language models designed for metadata extraction from scientific papers. The models, ranging from 0.5B to 3B parameters, are built by fine-tuning Qwen 2.5 counterparts. In their size family, MeXtract achieves state-of-the-art performance on metadata extraction on the MOLE benchmark. To further support evaluation, we extend the MOLE benchmark to incorporate model-specific metadata, providing an out-of-domain challenging subset. Our experiments show that fine-tuning on a given schema not only yields high accuracy but also transfers effectively to unseen schemas, demonstrating the robustness and adaptability of our approach. We release all the code, datasets, and models openly for the research community.
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            <a href="https://www.alphaxiv.org/abs/2510.06866v1" target="_blank" rel="noopener noreferrer">
                通过质量感知解码解锁大语言模型中的潜在话语翻译
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            Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wafaa Mohammed, Vlad Niculae, Chrysoula Zerva
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在翻译任务中的解码策略改进，属于纯粹的NLP应用领域。虽然质量感知解码技术本身具有通用性，但论文标题明确指向话语翻译应用，与推荐系统、搜索或广告的核心技术需求缺乏直接关联，潜在应用场景不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:37:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06866v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06866v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) have emerged as strong contenders in machine translation.Yet, they still struggle to adequately handle discourse phenomena, such as pronoun resolution and lexical cohesion at the document level. In this study, we thoroughly investigate the discourse phenomena performance of LLMs in context-aware translation. We demonstrate that discourse knowledge is encoded within LLMs and propose the use of quality-aware decoding (QAD) to effectively extract this knowledge, showcasing its superiority over other decoding approaches through comprehensive analysis. Furthermore, we illustrate that QAD enhances the semantic richness of translations and aligns them more closely with human preferences.
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            <a href="https://www.alphaxiv.org/abs/2510.06847v1" target="_blank" rel="noopener noreferrer">
                OpenJAI-v1.0：一个开源的泰语大语言模型
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            OpenJAI-v1.0: An Open Thai Large Language Model
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pontakorn Trakuekul, Attapol T. Rutherford, Jullajak Karnjanaekarin, Narongkorn ...
        </div>
        
        
        
        
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要介绍一个特定语言（泰语）的大语言模型，属于基础LLM技术开发。虽然LLM技术本身是相关领域，但该论文专注于特定语言实现，缺乏明确的推荐系统、搜索或广告应用场景的直接关联。作为语言特定的基础模型，其潜在应用可能包括多语言搜索或推荐，但论文标题未表明任何具体的应用方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:12:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06847v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06847v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce OpenJAI-v1.0, an open-source large language model for Thai and English, developed from the Qwen3-14B model. Our work focuses on boosting performance on practical tasks through carefully curated data across three key use cases: instruction following, long-context understanding, and tool use. Evaluation results show that OpenJAI-v1.0 improves on the capabilities of its base model and outperforms other leading open-source Thai models on a diverse suite of benchmarks, while avoiding catastrophic forgetting. OpenJAI-v1.0 is publicly released as another alternative NLP resource for the Thai AI community.
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            <a href="https://www.alphaxiv.org/abs/2510.06782v1" target="_blank" rel="noopener noreferrer">
                GPT-5模型修正了GPT-4V的图表读取错误，而非通过提示工程
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            GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaichun Yang, Jian Chen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态模型（GPT-4V）的图表理解错误修正，属于视觉-语言交互的特定问题，与推荐系统、搜索或广告的核心技术关联较弱。虽然涉及LLM模型迭代，但焦点是视觉内容理解的错误修正，而非在RecSys/Search/Ads领域的直接应用或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:09:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06782v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06782v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visualization questions to compare inference accuracies between the agentic GPT-5 and multimodal GPT-4V, for difficult image instances, where GPT-4V failed to produce correct answers. Our results show that model architecture dominates the inference accuracy: GPT5 largely improved accuracy, while prompt variants yielded only small effects. Pre-registration of this work is available here: https://osf.io/u78td/?view_only=6b075584311f48e991c39335c840ded3; the Google Drive materials are here:https://drive.google.com/file/d/1ll8WWZDf7cCNcfNWrLViWt8GwDNSvVrp/view.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06761v1" target="_blank" rel="noopener noreferrer">
                通过双循环多智能体协作演进与执行研究计划
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Evolving and Executing Research Plans via Double-Loop Multi-Agent Collaboration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhi Zhang, Yan Liu, Zhejing Hu, Gong Chen, Sheng-hua Zhong, Jiannong Cao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多智能体协作的研究计划制定与执行，属于通用AI系统架构范畴。虽然多智能体系统在理论上可以应用于推荐系统的决策优化，但论文标题没有明确指向推荐、搜索或广告领域的特定应用，也没有涉及LLM、Transformer架构或异构数据建模等核心技术。因此与当前关注点的直接相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:40:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06761v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06761v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automating the end-to-end scientific research process poses a fundamental challenge: it requires both evolving high-level plans that are novel and sound, and executing these plans correctly amidst dynamic and uncertain conditions. To address this bilevel challenge, we propose a novel Double-Loop Multi-Agent (DLMA) framework to solve the given research problem automatically. The leader loop, composed of professor agents, is responsible for evolving research plans. It employs an evolutionary algorithm through involvement, improvement, and integration meetings to iteratively generate and refine a pool of research proposals, exploring the solution space effectively. The follower loop, composed of doctoral student agents, is responsible for executing the best-evolved plan. It dynamically adjusts the plan during implementation via pre-hoc and post-hoc meetings, ensuring each step (e.g., drafting, coding) is well-supported by contextual and external observations. Extensive experiments on benchmarks like ACLAward and Laboratory show that DLMA generates research papers that achieve state-of-the-art scores in automated evaluation, significantly outperforming strong baselines. Ablation studies confirm the critical roles of both loops, with evolution driving novelty and execution ensuring soundness.
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            <a href="https://www.alphaxiv.org/abs/2510.06719v1" target="_blank" rel="noopener noreferrer">
                用于检索增强生成（RAG）的差分隐私合成文本生成
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junki Mori, Kazuya Kakizaki, Taiki Miyagawa, Jun Sakuma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注差分隐私技术，这属于隐私保护范畴，在无关主题中被明确排除。虽然RAG技术在搜索系统中可能有应用，但论文的核心焦点是隐私保护而非搜索/推荐系统的核心算法或架构改进。因此整体相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:15:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06719v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06719v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeated noise injection and leads to accumulated privacy loss. To address this issue, we propose DP-SynRAG, a framework that uses LLMs to generate differentially private synthetic RAG databases. Unlike prior methods, the synthetic text can be reused once created, thereby avoiding repeated noise injection and additional privacy costs. To preserve essential information for downstream RAG tasks, DP-SynRAG extends private prediction, which instructs LLMs to generate text that mimics subsampled database records in a DP manner. Experiments show that DP-SynRAG achieves superior performanec to the state-of-the-art private RAG systems while maintaining a fixed privacy budget, offering a scalable solution for privacy-preserving RAG.
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            <a href="https://www.alphaxiv.org/abs/2510.06700v1" target="_blank" rel="noopener noreferrer">
                语言模型如何混淆逻辑有效性与合理性：内容效应的表征分析
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leonardo Bertolazzi, Sandro Pezzelle, Raffaelle Bernardi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型在逻辑推理中的混淆问题，属于纯NLP领域的理论分析。虽然涉及语言模型的行为分析，但缺乏明确的推荐系统、搜索或广告应用场景，且不涉及Transformer架构改进或异质数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:48:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06700v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06700v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning problem influences judgments regarding its logical validity. While this phenomenon in humans is best explained by the dual-process theory of reasoning, the mechanisms behind content effects in LLMs remain unclear. In this work, we address this issue by investigating how LLMs encode the concepts of validity and plausibility within their internal representations. We show that both concepts are linearly represented and strongly aligned in representational geometry, leading models to conflate plausibility with validity. Using steering vectors, we demonstrate that plausibility vectors can causally bias validity judgements, and vice versa, and that the degree of alignment between these two concepts predicts the magnitude of behavioral content effects across models. Finally, we construct debiasing vectors that disentangle these concepts, reducing content effects and improving reasoning accuracy. Our findings advance understanding of how abstract logical concepts are represented in LLMs and highlight representational interventions as a path toward more logical systems.
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            <a href="https://www.alphaxiv.org/abs/2510.06677v1" target="_blank" rel="noopener noreferrer">
                基于渐进式笔记记录与智能体反馈的客户支持增量式摘要生成
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yisha Wu, Cen, Zhao, Yuanpei Cao, Xiaoqing Su, Yashar Mehdad, Mindy Ji, Claire N...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注客户支持场景下的增量式摘要生成技术，属于内容生成和对话系统的应用范畴。虽然客户支持可能与推荐/搜索系统有间接关联，但论文的核心技术（增量摘要、笔记记录）属于纯粹的LLM内容生成应用，不符合当前对推荐系统核心进展、Transformer架构改进或异构数据统一建模的关注重点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:05:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06677v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06677v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' context-switching effort and redundant review. Our approach combines a fine-tuned Mixtral-8x7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3% reduction in case handling time compared to bulk summarization (with reductions of up to 9% in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale.
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            <a href="https://www.alphaxiv.org/abs/2510.06664v1" target="_blank" rel="noopener noreferrer">
                ToolMem：通过可学习的工具能力记忆增强多模态智能体
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            ToolMem: Enhancing Multimodal Agents with Learnable Tool Capability Memory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunzhong Xiao, Yangmin Li, Hewei Wang, Yunlong Tang, Zora Zhiruo Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态智能体的工具使用能力记忆，属于通用AI智能体技术。虽然提到了工具能力记忆这一概念，但缺乏与推荐系统、搜索或广告领域的直接关联。多模态智能体技术可能在未来应用于更复杂的用户交互场景，但当前标题未显示出明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:32:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06664v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06664v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Agents utilizing tools powered by large language models (LLMs) or vision-language models (VLMs) have demonstrated remarkable progress in diverse tasks across text and visual modalities. Unlike traditional tools such as calculators, which give deterministic outputs, neural tools perform uncertainly across task scenarios. While different tools for a task may excel in varied scenarios, existing agents typically rely on fixed tools, thus limiting the flexibility in selecting the most suitable tool for specific tasks. In contrast, humans snowball their understanding of the capabilities of different tools by interacting with them, and apply this knowledge to select the optimal tool when solving a future task. To build agents that similarly benefit from this process, we propose ToolMem that enables agents to develop memories of tool capabilities from previous interactions, by summarizing their strengths and weaknesses and storing them in memory; at inference, the agent can retrieve relevant entries from ToolMem, and select the best tool to solve individual tasks more accurately. We evaluate ToolMem on learning varied text generation and text-to-image generation neural tools. Compared to no-memory, generic agents, we find ToolMem-augmented agents predict tool performance 14.8% and 28.7% more accurately across text and multimodal generation scenarios. Moreover, ToolMem facilitates optimal tool selection among multiple choices by 21% and 24% absolute increases in respective scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.06594v1" target="_blank" rel="noopener noreferrer">
                LLM内部层是否揭示越狱检测的模式？
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Do Internal Layers of LLMs Reveal Patterns for Jailbreak Detection?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM安全性和越狱检测，这属于安全/隐私相关主题，在无关主题列表中明确排除。虽然涉及LLM内部表征分析，但核心焦点是安全检测而非推荐系统、搜索或广告的应用。该研究没有展示在推荐、搜索或广告领域的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:55:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06594v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06594v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Jailbreaking large language models (LLMs) has emerged as a pressing concern with the increasing prevalence and accessibility of conversational LLMs. Adversarial users often exploit these models through carefully engineered prompts to elicit restricted or sensitive outputs, a strategy widely referred to as jailbreaking. While numerous defense mechanisms have been proposed, attackers continuously develop novel prompting techniques, and no existing model can be considered fully resistant. In this study, we investigate the jailbreak phenomenon by examining the internal representations of LLMs, with a focus on how hidden layers respond to jailbreak versus benign prompts. Specifically, we analyze the open-source LLM GPT-J and the state-space model Mamba2, presenting preliminary findings that highlight distinct layer-wise behaviors. Our results suggest promising directions for further research on leveraging internal model dynamics for robust jailbreak detection and defense.
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            <a href="https://www.alphaxiv.org/abs/2510.06579v1" target="_blank" rel="noopener noreferrer">
                TinyScientist：一个用于构建研究型智能体的交互式、可扩展且可控框架
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            TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注构建研究型智能体的通用框架，属于AIGC和智能体应用范畴，与推荐系统、搜索或广告的核心技术无直接关联。虽然框架的可控性和可扩展性可能间接应用于系统设计，但缺乏明确的RecSys/Search/Ads应用场景或核心技术关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:18:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06579v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06579v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, code execution, and human-agent interaction to accelerate research processes. However, as more researchers and developers begin to use and build upon these tools and platforms, the complexity and difficulty of extending and maintaining such agentic workflows have become a significant challenge, particularly as algorithms and architectures continue to advance. To address this growing complexity, TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, extensible, and controllable framework that easily adapts to new tools and supports iterative growth. We provide an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
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            <a href="https://www.alphaxiv.org/abs/2510.06559v1" target="_blank" rel="noopener noreferrer">
                意义的代数：为何机器需要蒙塔古胜过摩尔定律
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            The Algebra of Meaning: Why Machines Need Montague More Than Moore's Law
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheonkam Jeong, Sungdo Kim, Jewoo Park
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题暗示关注语义理解和形式逻辑（蒙塔古语义学），而非推荐系统、搜索或广告的核心技术领域。虽然语义理解在理论上与搜索相关，但论文似乎更偏向哲学/语言学基础，缺乏明确的实际应用指向，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 01:22:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06559v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06559v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Contemporary language models are fluent yet routinely mis-handle the types of meaning their outputs entail. We argue that hallucination, brittle moderation, and opaque compliance outcomes are symptoms of missing type-theoretic semantics rather than data or scale limitations. Building on Montague's view of language as typed, compositional algebra, we recast alignment as a parsing problem: natural-language inputs must be compiled into structures that make explicit their descriptive, normative, and legal dimensions under context. We present Savassan, a neuro-symbolic architecture that compiles utterances into Montague-style logical forms and maps them to typed ontologies extended with deontic operators and jurisdictional contexts. Neural components extract candidate structures from unstructured inputs; symbolic components perform type checking, constraint reasoning, and cross-jurisdiction mapping to produce compliance-aware guidance rather than binary censorship. In cross-border scenarios, the system "parses once" (e.g., defect claim(product x, company y)) and projects the result into multiple legal ontologies (e.g., defamation risk in KR/JP, protected opinion in US, GDPR checks in EU), composing outcomes into a single, explainable decision. This paper contributes: (i) a diagnosis of hallucination as a type error; (ii) a formal Montague-ontology bridge for business/legal reasoning; and (iii) a production-oriented design that embeds typed interfaces across the pipeline. We outline an evaluation plan using legal reasoning benchmarks and synthetic multi-jurisdiction suites. Our position is that trustworthy autonomy requires compositional typing of meaning, enabling systems to reason about what is described, what is prescribed, and what incurs liability within a unified algebra of meaning.
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            <a href="https://www.alphaxiv.org/abs/2510.07317v1" target="_blank" rel="noopener noreferrer">
                量子增强计算机视觉：超越经典算法
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            Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Natacha Kuete Meli, Shuteng Wang, Marcel Seelbach Benkner, Michele Sasdelli, Tat...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于量子计算在计算机视觉领域的应用，属于纯粹的视觉技术研究。虽然计算机视觉在搜索和推荐系统中可能有间接应用，但论文标题明确强调量子增强和超越经典算法，这属于量子计算与计算机视觉的交叉领域，与当前关注的LLM技术、Transformer架构、推荐系统核心进展等焦点领域相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07317v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07317v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing that leverages quantum-mechanical effects in computations inaccessible to classical (i.e. non-quantum) computers. In scenarios where existing non-quantum methods cannot find a solution in a reasonable time or compute only approximate solutions, quantum computers can provide, among others, advantages in terms of better time scalability for multiple problem classes. Parametrised quantum circuits can also become, in the long term, a considerable alternative to classical neural networks in computer vision. However, specialised and fundamentally new algorithms must be developed to enable compatibility with quantum hardware and unveil the potential of quantum computational paradigms in computer vision. This survey contributes to the existing literature on QeCV with a holistic review of this research field. It is designed as a quantum computing reference for the computer vision community, targeting computer vision students, scientists and readers with related backgrounds who want to familiarise themselves with QeCV. We provide a comprehensive introduction to QeCV, its specifics, and methodologies for formulations compatible with quantum hardware and QeCV methods, leveraging two main quantum computational paradigms, i.e. gate-based quantum computing and quantum annealing. We elaborate on the operational principles of quantum computers and the available tools to access, program and simulate them in the context of QeCV. Finally, we review existing quantum computing tools and learning materials and discuss aspects related to publishing and reviewing QeCV papers, open challenges and potential social implications.
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            <a href="https://www.alphaxiv.org/abs/2510.07316v1" target="_blank" rel="noopener noreferrer">
                基于语义提示扩散Transformer的像素级完美深度估计
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            Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gangwei Xu, Haotong Lin, Hongcheng Luo, Xianqi Wang, Jingfeng Yao, Lianghui Zhu,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的深度估计任务，使用扩散Transformer架构。虽然Transformer架构本身是相关技术，但该工作专注于纯粹的视觉深度估计，没有明确展示在推荐系统、搜索或广告中的潜在应用。深度估计主要应用于3D视觉、自动驾驶等领域，与当前关注的文本/序列建模应用场景不匹配。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07316v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07316v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps into latent space, which inevitably introduces \textit{flying pixels} at edges and details. Our model addresses this challenge by directly performing diffusion generation in the pixel space, avoiding VAE-induced artifacts. To overcome the high complexity associated with pixel-space generation, we introduce two novel designs: 1) Semantics-Prompted Diffusion Transformers (SP-DiT), which incorporate semantic representations from vision foundation models into DiT to prompt the diffusion process, thereby preserving global semantic consistency while enhancing fine-grained visual details; and 2) Cascade DiT Design that progressively increases the number of tokens to further enhance efficiency and accuracy. Our model achieves the best performance among all published generative models across five benchmarks, and significantly outperforms all other models in edge-aware point cloud evaluation.
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            <a href="https://www.alphaxiv.org/abs/2510.07310v1" target="_blank" rel="noopener noreferrer">
                MATRIX：面向交互感知视频生成的掩码轨迹对齐方法
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            MATRIX: Mask Track Alignment for Interaction-aware Video Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siyoon Jin, Seongchan Kim, Dahyun Chung, Jaeho Lee, Hyunwook Choi, Jisu Nam, Jiy...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成中的掩码轨迹对齐技术，属于计算机视觉领域。虽然标题提到'交互感知'，但这主要针对视频中的物体交互，与推荐系统、搜索或广告中的用户交互建模没有直接关联。该技术缺乏在异构数据处理或Transformer架构效率方面的明确应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:57:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07310v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07310v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether noun and verb tokens capture instances and their relations; and semantic propagation, via video-to-video attention, which assesses whether instance bindings persist across frames. We find both effects concentrate in a small subset of interaction-dominant layers. Motivated by this, we introduce MATRIX, a simple and effective regularization that aligns attention in specific layers of video DiTs with multi-instance mask tracks from the MATRIX-11K dataset, enhancing both grounding and propagation. We further propose InterGenEval, an evaluation protocol for interaction-aware video generation. In experiments, MATRIX improves both interaction fidelity and semantic alignment while reducing drift and hallucination. Extensive ablations validate our design choices. Codes and weights will be released.
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            <a href="https://www.alphaxiv.org/abs/2510.07206v1" target="_blank" rel="noopener noreferrer">
                EigenScore：基于扩散模型中协方差的分布外检测
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            EigenScore: OOD Detection using Covariance in Diffusion Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shirin Shoushtari, Yi Wang, Xiao Shi, M. Salman Asif, Ulugbek S. Kamilov
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型中的分布外检测技术，属于计算机视觉领域的特定应用。虽然扩散模型是生成模型的一种，但该工作专注于检测异常样本，与推荐系统、搜索或广告中的核心排名、用户建模或特征工程问题没有直接关联。该技术缺乏在推荐/搜索/广告领域的明确应用场景，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:42:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07206v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07206v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful generative models, capable of capturing complex data distributions through iterative denoising. Building on this progress, recent work has explored their potential for OOD detection. We propose EigenScore, a new OOD detection method that leverages the eigenvalue spectrum of the posterior covariance induced by a diffusion model. We argue that posterior covariance provides a consistent signal of distribution shift, leading to larger trace and leading eigenvalues on OOD inputs, yielding a clear spectral signature. We further provide analysis explicitly linking posterior covariance to distribution mismatch, establishing it as a reliable signal for OOD detection. To ensure tractability, we adopt a Jacobian-free subspace iteration method to estimate the leading eigenvalues using only forward evaluations of the denoiser. Empirically, EigenScore achieves SOTA performance, with up to 5% AUROC improvement over the best baseline. Notably, it remains robust in near-OOD settings such as CIFAR-10 vs CIFAR-100, where existing diffusion-based methods often fail.
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            <a href="https://www.alphaxiv.org/abs/2510.07143v1" target="_blank" rel="noopener noreferrer">
                我们是否使用了正确的基准：视觉令牌压缩方法的评估框架
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            <i class="fa fa-star mr-1"></i>2/10
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            Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhui...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉令牌压缩方法的评估框架，属于计算机视觉领域的基准测试研究。虽然视觉令牌压缩技术可能间接影响多模态推荐系统，但论文本身聚焦于纯视觉领域的评估基准，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:44:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07143v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07143v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent endeavors to accelerate inference in Multimodal Large Language Models (MLLMs) have primarily focused on visual token compression. The effectiveness of these methods is typically assessed by measuring the accuracy drop on established benchmarks, comparing model performance before and after compression. However, these benchmarks are originally designed to assess the perception and reasoning capabilities of MLLMs, rather than to evaluate compression techniques. As a result, directly applying them to visual token compression introduces a task mismatch. Strikingly, our investigation reveals that simple image downsampling consistently outperforms many advanced compression methods across multiple widely used benchmarks. Through extensive experiments, we make the following observations: (i) Current benchmarks are noisy for the visual token compression task. (ii) Down-sampling is able to serve as a data filter to evaluate the difficulty of samples in the visual token compression task. Motivated by these findings, we introduce VTC-Bench, an evaluation framework that incorporates a data filtering mechanism to denoise existing benchmarks, thereby enabling fairer and more accurate assessment of visual token compression methods. All data and code are available at https://github.com/Chenfei-Liao/VTC-Bench.
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            <a href="https://www.alphaxiv.org/abs/2510.07135v1" target="_blank" rel="noopener noreferrer">
                遥感视觉语言模型的少样本适应基准
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            Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Benoit Macq
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及视觉语言模型（VLM），但其专注于遥感领域的特定应用，这与推荐系统、搜索或广告的异构数据建模没有直接关联。遥感领域属于地理信息科学范畴，不在当前关注的核心领域范围内，且没有明确的机制表明其技术可迁移到商业推荐或搜索场景中。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:29:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07135v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07135v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data regimes, such as few-shot learning, remains insufficiently explored. In this work, we present the first structured benchmark for evaluating few-shot adaptation methods on RSVLMs. We conduct comprehensive experiments across ten remote sensing scene classification datasets, applying five widely used few-shot adaptation strategies to three state-of-the-art RSVLMs with varying backbones. Our findings reveal that models with similar zero-shot performance can exhibit markedly different behavior under few-shot adaptation, with some RSVLMs being inherently more amenable to such adaptation than others. The variability of performance and the absence of a clear winner among existing methods highlight the need for the development of more robust methods for few-shot adaptation tailored to RS. To facilitate future research, we provide a reproducible benchmarking framework and open-source code to systematically evaluate RSVLMs under few-shot conditions. The source code is publicly available on Github: https://github.com/elkhouryk/fewshot_RSVLMs
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            <a href="https://www.alphaxiv.org/abs/2510.07134v1" target="_blank" rel="noopener noreferrer">
                TrackVLA++：在视觉语言动作模型中释放推理和记忆能力以实现具身视觉跟踪
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            TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahang Liu, Yunpeng Qi, Jiazhao Zhang, Minghan Li, Shaoan Wang, Kui Wu, Hanjing...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于具身AI和视觉跟踪任务，属于机器人学和计算机视觉领域。虽然涉及视觉语言模型，但其核心应用是具身视觉跟踪而非推荐系统、搜索或广告场景。论文的推理和记忆能力增强主要服务于机器人导航和交互任务，与RecSys/Search/Ads的异构数据处理需求没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:29:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07134v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07134v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Embodied Visual Tracking (EVT) is a fundamental ability that underpins practical applications, such as companion robots, guidance robots and service assistants, where continuously following moving targets is essential. Recent advances have enabled language-guided tracking in complex and unstructured scenes. However, existing approaches lack explicit spatial reasoning and effective temporal memory, causing failures under severe occlusions or in the presence of similar-looking distractors. To address these challenges, we present TrackVLA++, a novel Vision-Language-Action (VLA) model that enhances embodied visual tracking with two key modules, a spatial reasoning mechanism and a Target Identification Memory (TIM). The reasoning module introduces a Chain-of-Thought paradigm, termed Polar-CoT, which infers the target's relative position and encodes it as a compact polar-coordinate token for action prediction. Guided by these spatial priors, the TIM employs a gated update strategy to preserve long-horizon target memory, ensuring spatiotemporal consistency and mitigating target loss during extended occlusions. Extensive experiments show that TrackVLA++ achieves state-of-the-art performance on public benchmarks across both egocentric and multi-camera settings. On the challenging EVT-Bench DT split, TrackVLA++ surpasses the previous leading approach by 5.1 and 12, respectively. Furthermore, TrackVLA++ exhibits strong zero-shot generalization, enabling robust real-world tracking in dynamic and occluded scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.07089v1" target="_blank" rel="noopener noreferrer">
                DADO：一种用于对象发现的深度注意力框架
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            DADO: A Depth-Attention framework for Object Discovery
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Federico Gonzalez, Estefania Talavera, Petia Radeva
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的对象发现任务，虽然涉及注意力机制，但缺乏与推荐系统、搜索或广告领域的明确联系。深度注意力框架可能对视觉内容理解有贡献，但论文标题未表明任何在异构数据建模或推荐相关应用方面的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:46:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07089v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07089v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in computer vision. In this work, we introduce a novel model, DADO (Depth-Attention self-supervised technique for Discovering unseen Objects), which combines an attention mechanism and a depth model to identify potential objects in images. To address challenges such as noisy attention maps or complex scenes with varying depth planes, DADO employs dynamic weighting to adaptively emphasize attention or depth features based on the global characteristics of each image. We evaluated DADO on standard benchmarks, where it outperforms state-of-the-art methods in object discovery accuracy and robustness without the need for fine-tuning.
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            <a href="https://www.alphaxiv.org/abs/2510.07077v1" target="_blank" rel="noopener noreferrer">
                面向机器人技术的视觉-语言-动作模型：迈向实际应用的研究综述
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        <div class="mb-2 text-base text-gray-700">
            Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kento Kawaharazuka, Jihoon Oh, Jun Yamada, Ingmar Posner, Yuke Zhu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注机器人技术领域的多模态模型，虽然涉及视觉-语言交互，但其核心应用场景是机器人控制和动作执行，与推荐系统、搜索或广告的关联性较弱。论文可能包含一些多模态建模技术，但这些技术在RecSys/Search/Ads中的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:38:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07077v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07077v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Amid growing efforts to leverage advances in large language models (LLMs) and vision-language models (VLMs) for robotics, Vision-Language-Action (VLA) models have recently gained significant attention. By unifying vision, language, and action data at scale, which have traditionally been studied separately, VLA models aim to learn policies that generalise across diverse tasks, objects, embodiments, and environments. This generalisation capability is expected to enable robots to solve novel downstream tasks with minimal or no additional task-specific data, facilitating more flexible and scalable real-world deployment. Unlike previous surveys that focus narrowly on action representations or high-level model architectures, this work offers a comprehensive, full-stack review, integrating both software and hardware components of VLA systems. In particular, this paper provides a systematic review of VLAs, covering their strategy and architectural transition, architectures and building blocks, modality-specific processing techniques, and learning paradigms. In addition, to support the deployment of VLAs in real-world robotic applications, we also review commonly used robot platforms, data collection strategies, publicly available datasets, data augmentation methods, and evaluation benchmarks. Throughout this comprehensive survey, this paper aims to offer practical guidance for the robotics community in applying VLAs to real-world robotic systems. All references categorized by training approach, evaluation method, modality, and dataset are available in the table on our project website: https://vla-survey.github.io .
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            <a href="https://www.alphaxiv.org/abs/2510.07053v1" target="_blank" rel="noopener noreferrer">
                习得语义场景图定位中的自省机制
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            Introspection in Learned Semantic Scene Graph Localisation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的场景图定位和自省机制，属于纯粹的视觉研究方向。虽然场景图在概念上涉及语义理解，但论文标题明确聚焦于视觉场景定位任务，没有显示出与推荐系统、搜索或广告的直接关联，也没有涉及Transformer架构改进或LLM技术应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:21:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07053v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07053v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span><span class="category-tag">I.2.10; I.2.9; I.4.8; I.5.2; I.5.1</span></div>
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                    This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. Integrated gradients and Attention Weights consistently emerge as the most reliable probes of learned behaviour. A semantic class ablation further reveals an implicit weighting in which frequent objects are often down-weighted. Overall, the results indicate that the model learns noise-robust, semantically salient relations about place definition, thereby enabling explainable registration under challenging visual and structural variations.
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            <a href="https://www.alphaxiv.org/abs/2510.07018v1" target="_blank" rel="noopener noreferrer">
                面向零样本量化的锐度感知数据生成
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Sharpness-Aware Data Generation for Zero-shot Quantization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dung Hoang-Anh, Cuong Pham Trung Le, Jianfei Cai, Thanh-Toan Do
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注模型量化的数据生成方法，虽然量化技术可能间接应用于推荐或搜索系统中的模型部署效率，但论文核心焦点是通用的模型压缩而非特定于推荐/搜索/广告领域的应用。作为使能技术，其潜在应用场景过于宽泛，缺乏与推荐系统、搜索或广告领域的直接关联性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:43:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07018v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07018v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Zero-shot quantization aims to learn a quantized model from a pre-trained full-precision model with no access to original real training data. The common idea in zero-shot quantization approaches is to generate synthetic data for quantizing the full-precision model. While it is well-known that deep neural networks with low sharpness have better generalization ability, none of the previous zero-shot quantization works considers the sharpness of the quantized model as a criterion for generating training data. This paper introduces a novel methodology that takes into account quantized model sharpness in synthetic data generation to enhance generalization. Specifically, we first demonstrate that sharpness minimization can be attained by maximizing gradient matching between the reconstruction loss gradients computed on synthetic and real validation data, under certain assumptions. We then circumvent the problem of the gradient matching without real validation set by approximating it with the gradient matching between each generated sample and its neighbors. Experimental evaluations on CIFAR-100 and ImageNet datasets demonstrate the superiority of the proposed method over the state-of-the-art techniques in low-bit quantization settings.
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            <a href="https://www.alphaxiv.org/abs/2510.06955v1" target="_blank" rel="noopener noreferrer">
                高比率混合丢弃：重新审视混合丢弃以实现鲁棒的领域泛化
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            High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注领域泛化和正则化技术（Mixout），这属于通用的机器学习方法，与推荐系统、搜索或广告的核心进展没有直接关联。虽然正则化技术可能间接影响模型性能，但论文没有明确展示在RecSys/Search/Ads领域的潜在应用或具体相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:37:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06955v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06955v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial computational costs due to the need to train and store multiple models. Dropout offers a lightweight alternative by simulating ensembles through random neuron deactivation; however, when applied to pre-trained models, it tends to over-regularize and disrupt critical representations necessary for generalization. In this work, we investigate Mixout, a stochastic regularization technique that provides an alternative to Dropout for domain generalization. Rather than deactivating neurons, Mixout mitigates overfitting by probabilistically swapping a subset of fine-tuned weights with their pre-trained counterparts during training, thereby maintaining a balance between adaptation and retention of prior knowledge. Our study reveals that achieving strong performance with Mixout on domain generalization benchmarks requires a notably high masking probability of 0.9 for ViTs and 0.8 for ResNets. While this may seem like a simple adjustment, it yields two key advantages for domain generalization: (1) higher masking rates more strongly penalize deviations from the pre-trained parameters, promoting better generalization to unseen domains; and (2) high-rate masking substantially reduces computational overhead, cutting gradient computation by up to 45% and gradient memory usage by up to 90%. Experiments across five domain generalization benchmarks, PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet, using ResNet and ViT architectures, show that our approach, High-rate Mixout, achieves out-of-domain accuracy comparable to ensemble-based methods while significantly reducing training costs.
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            <a href="https://www.alphaxiv.org/abs/2510.06928v1" target="_blank" rel="noopener noreferrer">
                IAR2：通过语义-细节关联的令牌预测改进自回归视觉生成
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            IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ran Yi, Teng Hu, Zihan Su, Lizhuang Ma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉生成领域的自回归模型改进，属于纯粹的视觉生成技术。虽然提到了令牌预测技术，但核心应用场景是视觉内容生成而非推荐系统、搜索或广告。该技术缺乏明确的在RecSys/Search/Ads领域的潜在应用路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:08:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06928v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06928v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Autoregressive models have emerged as a powerful paradigm for visual content creation, but often overlook the intrinsic structural properties of visual data. Our prior work, IAR, initiated a direction to address this by reorganizing the visual codebook based on embedding similarity, thereby improving generation robustness. However, it is constrained by the rigidity of pre-trained codebooks and the inaccuracies of hard, uniform clustering. To overcome these limitations, we propose IAR2, an advanced autoregressive framework that enables a hierarchical semantic-detail synthesis process. At the core of IAR2 is a novel Semantic-Detail Associated Dual Codebook, which decouples image representations into a semantic codebook for global semantic information and a detail codebook for fine-grained refinements. It expands the quantization capacity from a linear to a polynomial scale, significantly enhancing expressiveness. To accommodate this dual representation, we propose a Semantic-Detail Autoregressive Prediction scheme coupled with a Local-Context Enhanced Autoregressive Head, which performs hierarchical prediction-first the semantic token, then the detail token-while leveraging a local context window to enhance spatial coherence. Furthermore, for conditional generation, we introduce a Progressive Attention-Guided Adaptive CFG mechanism that dynamically modulates the guidance scale for each token based on its relevance to the condition and its temporal position in the generation sequence, improving conditional alignment without sacrificing realism. Extensive experiments demonstrate that IAR2 sets a new state-of-the-art for autoregressive image generation, achieving a FID of 1.50 on ImageNet. Our model not only surpasses previous methods in performance but also demonstrates superior computational efficiency, highlighting the effectiveness of our structured, coarse-to-fine generation strategy.
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            <a href="https://www.alphaxiv.org/abs/2510.06926v1" target="_blank" rel="noopener noreferrer">
                基于生成式虚拟范例学习的标签节约型卫星图像变化检测
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Label-frugal satellite image change detection with generative virtual exemplar learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hichem Sahbi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注卫星图像变化检测的计算机视觉任务，虽然涉及生成式学习和数据效率技术，但与推荐系统、搜索或广告的核心领域缺乏直接关联。生成式虚拟范例学习技术可能对数据增强有启发，但在当前RecSys/Search/Ads应用场景中的潜在价值有限且不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:07:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06926v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06926v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user (oracle). In this paper, we devise a novel change detection algorithm, based on active learning. The main contribution of our work resides in a new model that measures how important is each unlabeled sample, and provides an oracle with only the most critical samples (also referred to as virtual exemplars) for further labeling. These exemplars are generated, using an invertible graph convnet, as the optimum of an adversarial loss that (i) measures representativity, diversity and ambiguity of the data, and thereby (ii) challenges (the most) the current change detection criteria, leading to a better re-estimate of these criteria in the subsequent iterations of active learning. Extensive experiments show the positive impact of our label-efficient learning model against comparative methods.
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            <a href="https://www.alphaxiv.org/abs/2510.06907v1" target="_blank" rel="noopener noreferrer">
                基于SpherePair损失的角约束嵌入用于约束聚类
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Angular Constraint Embedding via SpherePair Loss for Constrained Clustering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaojie Zhang, Ke Chen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注约束聚类中的嵌入方法，虽然嵌入技术在推荐系统中用于表示学习，但该工作更偏向通用的聚类任务而非特定的RecSys/Search/Ads应用。角约束和SpherePair损失是通用的表示学习技术，没有明确展示在推荐、搜索或广告领域的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:43:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06907v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06907v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Constrained clustering integrates domain knowledge through pairwise constraints. However, existing deep constrained clustering (DCC) methods are either limited by anchors inherent in end-to-end modeling or struggle with learning discriminative Euclidean embedding, restricting their scalability and real-world applicability. To avoid their respective pitfalls, we propose a novel angular constraint embedding approach for DCC, termed SpherePair. Using the SpherePair loss with a geometric formulation, our method faithfully encodes pairwise constraints and leads to embeddings that are clustering-friendly in angular space, effectively separating representation learning from clustering. SpherePair preserves pairwise relations without conflict, removes the need to specify the exact number of clusters, generalizes to unseen data, enables rapid inference of the number of clusters, and is supported by rigorous theoretical guarantees. Comparative evaluations with state-of-the-art DCC methods on diverse benchmarks, along with empirical validation of theoretical insights, confirm its superior performance, scalability, and overall real-world effectiveness. Code is available at \href{https://github.com/spherepaircc/SpherePairCC/tree/main}{our repository}.
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                SaFeR-VLM：面向多模态模型中安全感知的细粒度推理
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        <div class="mb-2 text-base text-gray-700">
            SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huahui Yi, Kun Wang, Qiankun Li, Miao Yu, Liang Lin, Gongli Xi, Hao Wu, Xuming H...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及多模态模型和推理技术，但其核心关注点是安全性和风险控制，这属于被排除的隐私/安全/伦理范畴。论文标题明确强调'safety-aware'（安全感知），表明其主要目标是模型安全性而非推荐/搜索/广告系统的核心技术进步。在细粒度推理方面可能有间接价值，但安全焦点使其与当前关注点相关性很低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:39:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06871v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06871v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Multimodal Large Reasoning Models (MLRMs) demonstrate impressive cross-modal reasoning but often amplify safety risks under adversarial or unsafe prompts, a phenomenon we call the \textit{Reasoning Tax}. Existing defenses mainly act at the output level and do not constrain the reasoning process, leaving models exposed to implicit risks. In this paper, we propose SaFeR-VLM, a safety-aligned reinforcement learning framework that embeds safety directly into multimodal reasoning. The framework integrates four components: (I) QI-Safe-10K, a curated dataset emphasizing safety-critical and reasoning-sensitive cases; (II) safety-aware rollout, where unsafe generations undergo reflection and correction instead of being discarded; (III) structured reward modeling with multi-dimensional weighted criteria and explicit penalties for hallucinations and contradictions; and (IV) GRPO optimization, which reinforces both safe and corrected trajectories. This unified design shifts safety from a passive safeguard to an active driver of reasoning, enabling scalable and generalizable safety-aware reasoning. SaFeR-VLM further demonstrates robustness against both explicit and implicit risks, supporting dynamic and interpretable safety decisions beyond surface-level filtering. SaFeR-VLM-3B achieves average performance $70.13$ and $78.97$ on safety and helpfulness across six benchmarks, surpassing both same-scale and $>10\times$ larger models such as Skywork-R1V3-38B, Qwen2.5VL-72B, and GLM4.5V-106B. Remarkably, SaFeR-VLM-7B benefits from its increased scale to surpass GPT-5-mini and Gemini-2.5-Flash by \num{6.47} and \num{16.76} points respectively on safety metrics, achieving this improvement without any degradation in helpfulness performance. Our codes are available at https://github.com/HarveyYi/SaFeR-VLM.
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            <a href="https://www.alphaxiv.org/abs/2510.06842v1" target="_blank" rel="noopener noreferrer">
                基于自适应流形对齐图正则化的持续动作质量评估
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kanglei Zhou, Qingyi Pan, Xingxing Zhang, Hubert P. H. Shum, Frederick W. B. Li,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注动作质量评估的持续学习问题，这属于计算机视觉和时序分析领域，与推荐系统、搜索或广告的核心技术关联性较弱。虽然图正则化技术在某些推荐场景中有应用，但论文的特定应用领域（动作质量评估）与当前关注的LLM技术、Transformer架构或异构数据建模没有直接联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:09:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06842v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06842v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation. A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios, which limits the generalization ability of conventional methods. We introduce Continual AQA (CAQA), which equips AQA with Continual Learning (CL) capabilities to handle evolving distributions while mitigating catastrophic forgetting. Although parameter-efficient fine-tuning of pretrained models has shown promise in CL for image classification, we find it insufficient for CAQA. Our empirical and theoretical analyses reveal two insights: (i) Full-Parameter Fine-Tuning (FPFT) is necessary for effective representation learning; yet (ii) uncontrolled FPFT induces overfitting and feature manifold shift, thereby aggravating forgetting. To address this, we propose Adaptive Manifold-Aligned Graph Regularization (MAGR++), which couples backbone fine-tuning that stabilizes shallow layers while adapting deeper ones with a two-step feature rectification pipeline: a manifold projector to translate deviated historical features into the current representation space, and a graph regularizer to align local and global distributions. We construct four CAQA benchmarks from three datasets with tailored evaluation protocols and strong baselines, enabling systematic cross-dataset comparison. Extensive experiments show that MAGR++ achieves state-of-the-art performance, with average correlation gains of 3.6% offline and 12.2% online over the strongest baseline, confirming its robustness and effectiveness. Our code is available at https://github.com/ZhouKanglei/MAGRPP.
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            <a href="https://www.alphaxiv.org/abs/2510.06827v1" target="_blank" rel="noopener noreferrer">
                StyleKeeper：使用负向视觉查询引导防止内容泄露
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jaeseok Jeong, Junho Kim, Gayoung Lee, Yunjey Choi, Youngjung Uh
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及内容保护和视觉查询技术，主要关注防止内容泄露的安全问题，这属于隐私和安全范畴，在无关主题中明确排除。虽然提到了视觉查询，但没有明确与推荐系统、搜索或广告的关联，且安全防护不属于当前技术焦点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:50:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06827v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06827v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled more precise control over style and content. However, existing methods often suffer from content leakage, where undesired elements of the visual style prompt are transferred along with the intended style. To address this issue, we 1) extend classifier-free guidance (CFG) to utilize swapping self-attention and propose 2) negative visual query guidance (NVQG) to reduce the transfer of unwanted contents. NVQG employs negative score by intentionally simulating content leakage scenarios that swap queries instead of key and values of self-attention layers from visual style prompts. This simple yet effective method significantly reduces content leakage. Furthermore, we provide careful solutions for using a real image as visual style prompts. Through extensive evaluation across various styles and text prompts, our method demonstrates superiority over existing approaches, reflecting the style of the references, and ensuring that resulting images match the text prompts. Our code is available \href{https://github.com/naver-ai/StyleKeeper}{here}.
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            <a href="https://www.alphaxiv.org/abs/2510.06783v1" target="_blank" rel="noopener noreferrer">
                TTRV：视觉语言模型的测试时强化学习
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            TTRV: Test-Time Reinforcement Learning for Vision Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Akshit Singh, Shyam Marjit, Wei Lin, Paul Gavrikov, Serena Yeung-Levy, Hilde Kue...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型的测试时强化学习，属于纯粹的视觉-语言交叉领域研究。虽然提到了强化学习，但没有明确展示与推荐系统、搜索或广告的相关性。测试时强化学习技术可能对模型在线适应有一定启发，但论文标题未表明任何与推荐/搜索/广告领域的直接联系或潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:10:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06783v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06783v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn directly from their environment. In this work, we propose TTRV to enhance vision language understanding by adapting the model on the fly at inference time, without the need for any labeled data. Concretely, we enhance the Group Relative Policy Optimization (GRPO) framework by designing rewards based on the frequency of the base model's output, while inferring on each test sample multiple times. Further, we also propose to control the diversity of the model's output by simultaneously rewarding the model for obtaining low entropy of the output empirical distribution. Our approach delivers consistent gains across both object recognition and visual question answering (VQA), with improvements of up to 52.4% and 29.8%, respectively, and average boosts of 24.6% and 10.0% across 16 datasets.Remarkably, on image recognition, TTRV applied to InternVL 8B surpasses GPT-4o by an average of 2.3% over 8 benchmarks, while remaining highly competitive on VQA, demonstrating that test-time reinforcement learning can match or exceed the strongest proprietary models. Finally, we find many interesting properties of test-time RL for VLMs: for example, even in extremely data-constrained scenarios, where adaptation is performed on a single randomly chosen unlabeled test example, TTRV still yields non-trivial improvements of up to 5.5% in recognition tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.06746v1" target="_blank" rel="noopener noreferrer">
                DeRainMamba：一种用于图像去雨的频率感知状态空间模型与细节增强方法
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            DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiliang Zhu, Tao Zeng, Tao Yang, Guoliang Luo, Jiyong Zeng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像去雨任务，属于纯粹的视觉处理领域。虽然状态空间模型是Transformer的替代架构，但该工作没有展示与推荐系统、搜索或广告的明显关联。图像去雨本身在RecSys/Search/Ads领域缺乏直接应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:05:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06746v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06746v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image deraining is crucial for improving visual quality and supporting reliable downstream vision tasks. Although Mamba-based models provide efficient sequence modeling, their limited ability to capture fine-grained details and lack of frequency-domain awareness restrict further improvements. To address these issues, we propose DeRainMamba, which integrates a Frequency-Aware State-Space Module (FASSM) and Multi-Directional Perception Convolution (MDPConv). FASSM leverages Fourier transform to distinguish rain streaks from high-frequency image details, balancing rain removal and detail preservation. MDPConv further restores local structures by capturing anisotropic gradient features and efficiently fusing multiple convolution branches. Extensive experiments on four public benchmarks demonstrate that DeRainMamba consistently outperforms state-of-the-art methods in PSNR and SSIM, while requiring fewer parameters and lower computational costs. These results validate the effectiveness of combining frequency-domain modeling and spatial detail enhancement within a state-space framework for single image deraining.
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            <a href="https://www.alphaxiv.org/abs/2510.06679v1" target="_blank" rel="noopener noreferrer">
                DreamOmni2：基于多模态指令的编辑与生成
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            DreamOmni2: Multimodal Instruction-based Editing and Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bin Xia, Bohao Peng, Yuechen Zhang, Junjia Huang, Jiyang Liu, Jingyao Li, Haoru ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态内容的编辑和生成，属于纯粹的AIGC和内容生成领域，与推荐系统、搜索或广告的核心排序任务没有直接关联。虽然多模态技术本身有潜力，但该论文的应用方向更偏向内容创作而非用户行为建模或个性化推荐，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:07:14
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                <a href="https://arxiv.org/abs/2510.06679v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06679v1
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical user needs. Instruction-based editing relies solely on language instructions, which often fail to capture specific editing details, making reference images necessary. Meanwhile, subject-driven generation is limited to combining concrete objects or people, overlooking broader, abstract concepts. To address these challenges, we propose two novel tasks: multimodal instruction-based editing and generation. These tasks support both text and image instructions and extend the scope to include both concrete and abstract concepts, greatly enhancing their practical applications. We introduce DreamOmni2, tackling two primary challenges: data creation and model framework design. Our data synthesis pipeline consists of three steps: (1) using a feature mixing method to create extraction data for both abstract and concrete concepts, (2) generating multimodal instruction-based editing training data using the editing and extraction models, and (3) further applying the extraction model to create training data for multimodal instruction-based editing. For the framework, to handle multi-image input, we propose an index encoding and position encoding shift scheme, which helps the model distinguish images and avoid pixel confusion. Additionally, we introduce joint training with the VLM and our generation/editing model to better process complex instructions. In addition, we have proposed comprehensive benchmarks for these two new tasks to drive their development. Experiments show that DreamOmni2 has achieved impressive results. Models and codes will be released.
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            <a href="https://www.alphaxiv.org/abs/2510.06646v1" target="_blank" rel="noopener noreferrer">
                机器学习算子中零样本超分辨率的虚假承诺
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        <div class="mb-2 text-base text-gray-700">
            The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mansi Sakarvadia, Kareem Hegazy, Amin Totounferoush, Kyle Chard, Yaoqing Yang, I...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要讨论机器学习算子中的零样本超分辨率问题，这属于计算机视觉领域的特定技术应用。虽然超分辨率技术理论上可能用于提升推荐系统中的图像特征质量，但论文标题明确聚焦于该技术的局限性而非实际应用，与推荐系统、搜索或广告的核心技术进展关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:59:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06646v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06646v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    A core challenge in scientific machine learning, and scientific computing more generally, is modeling continuous phenomena which (in practice) are represented discretely. Machine-learned operators (MLOs) have been introduced as a means to achieve this modeling goal, as this class of architecture can perform inference at arbitrary resolution. In this work, we evaluate whether this architectural innovation is sufficient to perform "zero-shot super-resolution," namely to enable a model to serve inference on higher-resolution data than that on which it was originally trained. We comprehensively evaluate both zero-shot sub-resolution and super-resolution (i.e., multi-resolution) inference in MLOs. We decouple multi-resolution inference into two key behaviors: 1) extrapolation to varying frequency information; and 2) interpolating across varying resolutions. We empirically demonstrate that MLOs fail to do both of these tasks in a zero-shot manner. Consequently, we find MLOs are not able to perform accurate inference at resolutions different from those on which they were trained, and instead they are brittle and susceptible to aliasing. To address these failure modes, we propose a simple, computationally-efficient, and data-driven multi-resolution training protocol that overcomes aliasing and that provides robust multi-resolution generalization.
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            <a href="https://www.alphaxiv.org/abs/2510.06638v1" target="_blank" rel="noopener noreferrer">
                StaR-KVQA：面向隐式知识视觉问答的结构化推理轨迹
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            StaR-KVQA: Structured Reasoning Traces for Implicit-Knowledge Visual Question Answering
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhihao Wen, Wenkang Wei, Yuan Fang, Xingtong Yu, Hui Zhang, Weicheng Zhu, Xin Zh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉问答中的隐式知识推理和结构化推理轨迹，属于纯粹的视觉-语言交叉领域研究。虽然标题提及结构化推理，但其核心应用场景是视觉问答，与推荐系统、搜索或广告中的异构数据建模没有直接关联，也不涉及Transformer架构改进或LLM技术在推荐领域的直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:37:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06638v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06638v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge-based Visual Question Answering (KVQA) requires models to ground entities in images and reason over factual knowledge. We study its implicit-knowledge variant, IK-KVQA, where a multimodal large language model (MLLM) is the sole knowledge source, without external retrieval. Yet, MLLMs lack explicit reasoning supervision and produce inconsistent justifications, and generalize poorly after standard supervised fine-tuning (SFT). We present StaR-KVQA (Structured Reasoning Traces for IK-KVQA), which supervises structured traces - dual symbolic relation paths plus path-grounded natural-language explanations - so that reasoning becomes transparent and verifiable. With one open-source MLLM, StaR-KVQA constructs and selects path-grounded reasoning traces to form a trace-enriched dataset, then fine-tunes via structured self-distillation to align generation with supervision; no external retrievers, verifiers, or curated knowledge bases (KBs) are used, traces are built offline, and inference is a single autoregressive pass. Across benchmarks, StaR-KVQA improves both accuracy and interpretability, achieving up to +11.3% higher answer accuracy on OK-VQA over the strongest baseline while exhibiting robust cross-domain generalization.
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            <a href="https://www.alphaxiv.org/abs/2510.06637v1" target="_blank" rel="noopener noreferrer">
                用于数据同化的控制增强自回归扩散模型
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            Control-Augmented Autoregressive Diffusion for Data Assimilation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Prakhar Srivastava, Farrin Marouf Sofian, Francesco Immorlano, Kushagra Pandey, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注数据同化中的扩散模型技术，属于生成模型领域。虽然扩散模型在序列生成方面有潜力，但论文标题未明确表明与推荐系统、搜索或广告的直接关联，且缺乏明确的Transformer架构或LLM应用连接。其潜在应用场景过于宽泛，在当前聚焦领域中的相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:37:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06637v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06637v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Despite recent advances in test-time scaling and finetuning of diffusion models, guidance in Auto-Regressive Diffusion Models (ARDMs) remains underexplored. We introduce an amortized framework that augments pretrained ARDMs with a lightweight controller network, trained offline by previewing future ARDM rollouts and learning stepwise controls that anticipate upcoming observations under a terminal cost objective. We evaluate this framework in the context of data assimilation (DA) for chaotic spatiotemporal partial differential equations (PDEs), a setting where existing methods are often computationally prohibitive and prone to forecast drift under sparse observations. Our approach reduces DA inference to a single forward rollout with on-the-fly corrections, avoiding expensive adjoint computations and/or optimizations during inference. We demonstrate that our method consistently outperforms four state-of-the-art baselines in stability, accuracy, and physical fidelity across two canonical PDEs and six observation regimes. We will release code and checkpoints publicly.
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            <a href="https://www.alphaxiv.org/abs/2510.06635v1" target="_blank" rel="noopener noreferrer">
                StruSR：具有物理信息泰勒引导的结构感知符号回归
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            StruSR: Structure-Aware Symbolic Regression with Physics-Informed Taylor Guidance
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunpeng Gong, Sihan Lan, Can Yang, Kunpeng Xu, Min Jiang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注符号回归和物理信息引导的数学建模，属于通用机器学习方法学范畴。虽然符号回归在理论上可以用于发现推荐系统中的复杂特征交互模式，但论文的物理信息导向和泰勒展开方法更偏向科学计算和物理建模领域，与推荐系统、搜索或广告的核心技术栈关联度较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:37:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06635v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06635v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Symbolic regression aims to find interpretable analytical expressions by searching over mathematical formula spaces to capture underlying system behavior, particularly in scientific modeling governed by physical laws. However, traditional methods lack mechanisms for extracting structured physical priors from time series observations, making it difficult to capture symbolic expressions that reflect the system's global behavior. In this work, we propose a structure-aware symbolic regression framework, called StruSR, that leverages trained Physics-Informed Neural Networks (PINNs) to extract locally structured physical priors from time series data. By performing local Taylor expansions on the outputs of the trained PINN, we obtain derivative-based structural information to guide symbolic expression evolution. To assess the importance of expression components, we introduce a masking-based attribution mechanism that quantifies each subtree's contribution to structural alignment and physical residual reduction. These sensitivity scores steer mutation and crossover operations within genetic programming, preserving substructures with high physical or structural significance while selectively modifying less informative components. A hybrid fitness function jointly minimizes physics residuals and Taylor coefficient mismatch, ensuring consistency with both the governing equations and the local analytical behavior encoded by the PINN. Experiments on benchmark PDE systems demonstrate that StruSR improves convergence speed, structural fidelity, and expression interpretability compared to conventional baselines, offering a principled paradigm for physics-grounded symbolic discovery.
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            <a href="https://www.alphaxiv.org/abs/2510.06590v1" target="_blank" rel="noopener noreferrer">
                Ming-UniVision：基于统一连续分词器的联合图像理解与生成
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            Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyuan Huang, DanDan Zheng, Cheng Zou, Rui Liu, Xiaolong Wang, Kaixiang Ji, Weil...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉领域的联合理解与生成任务，属于纯粹的视觉和生成式AI范畴。虽然标题提到'统一分词器'技术，但核心应用场景是图像处理而非推荐/搜索/广告系统，且没有明确展示这些技术如何应用于异构数据处理或推荐排序任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:50:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06590v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06590v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in discrete latent spaces to align with the tokens from large language models, where the quantization errors can limit semantic expressiveness and degrade the capability of vision-language understanding. To address this, we introduce MingTok, a new family of visual tokenizers with a continuous latent space, for unified autoregressive generation and understanding. While understanding tasks favor discriminative high-dimensional features, generation tasks prefer compact low-level codes. Thus, to reconcile these competing demands, MingTok adopts a three-stage sequential architecture involving low-level encoding, semantic expansion, and visual reconstruction. Built on top of it, Ming-UniVision eliminates the need for task-specific visual representations, and unifies diverse vision-language tasks under a single autoregrsssive prediction paradigm. By formulating both understanding and generation as next-token prediction in a shared continuous space, it seamlessly supports multi-round, in-context tasks such as iterative understanding, generation and editing. Empirically, we find that using a unified continuous visual representation reconciles the competing requirements on the tokenizers by the understanding and generation tasks, thereby leading to state-of-the-art level performance across both domains. We hope our findings will facilitate unified visual tokenization in the continuous domain. Inference code and model weights are released to benefit community.
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                簇路径：神经网络可解释性导航
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            Cluster Paths: Navigating Interpretability in Neural Networks
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nicholas M. Kroeger, Vincent Bindschaedler
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注神经网络的可解释性，这是一个通用的机器学习主题，而非专门针对推荐系统、搜索或广告领域的核心进展。虽然可解释性在模型调试中有一定价值，但论文没有明确展示与Transformer架构效率、LLM技术应用或异构数据统一建模的直接关联，因此相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 00:41:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06541v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06541v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While modern deep neural networks achieve impressive performance in vision tasks, they remain opaque in their decision processes, risking unwarranted trust, undetected biases and unexpected failures. We propose cluster paths, a post-hoc interpretability method that clusters activations at selected layers and represents each input as its sequence of cluster IDs. To assess these cluster paths, we introduce four metrics: path complexity (cognitive load), weighted-path purity (class alignment), decision-alignment faithfulness (predictive fidelity), and path agreement (stability under perturbations). In a spurious-cue CIFAR-10 experiment, cluster paths identify color-based shortcuts and collapse when the cue is removed. On a five-class CelebA hair-color task, they achieve 90% faithfulness and maintain 96% agreement under Gaussian noise without sacrificing accuracy. Scaling to a Vision Transformer pretrained on ImageNet, we extend cluster paths to concept paths derived from prompting a large language model on minimal path divergences. Finally, we show that cluster paths can serve as an effective out-of-distribution (OOD) detector, reliably flagging anomalous samples before the model generates over-confident predictions. Cluster paths uncover visual concepts, such as color palettes, textures, or object contexts, at multiple network depths, demonstrating that cluster paths scale to large vision models while generating concise and human-readable explanations.
                </div>
            </details>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06529v1" target="_blank" rel="noopener noreferrer">
                VUGEN：用于生成的视觉理解先验
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            VUGEN: Visual Understanding priors for GENeration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangyi Chen, Théophane Vallaeys, Maha Elbayad, John Nguyen, Jakob Verbeek
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉理解和生成任务，属于纯粹的视觉-语言模型领域。虽然标题提到了生成能力，但这更偏向于内容生成（AIGC）方向，而非推荐系统、搜索或广告中的排名和建模任务。在推荐/搜索场景中，视觉理解可能用于商品图像理解，但论文核心焦点不在这些应用上。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 00:04:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06529v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06529v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in Vision-Language Models (VLMs) have enabled unified understanding across text and images, yet equipping these models with robust image generation capabilities remains challenging. Existing approaches often rely on reconstruction-oriented autoencoders or complex bridging mechanisms, leading to misalignment between understanding and generation representations, or architectural complexity. In this work, we propose VUGEN, a novel framework that explicitly leverages VLM's pretrained visual understanding priors for efficient and high-quality image generation. Our approach first transforms the high-dimensional latent space of the VLM's native vision encoder into a lower-dimensional, tractable distribution that maximally preserves visual information. The VLM is then trained to sample within this reduced latent space, ensuring alignment with its visual understanding capabilities. Finally, a dedicated pixel decoder maps these generated latents back to the image space. We find that a VAE-free pixel diffusion decoder to be on par or better than commonly used complex latent diffusion decoders that internally rely on VAE latents. Extensive experiments demonstrate that VUGEN achieves superior image generation performance, improving DPG Bench from 71.17 to 74.32 and FID from 11.86 to 9.06 on COCO, while fully preserving the VLM's original understanding capabilities.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06924v1" target="_blank" rel="noopener noreferrer">
                基于协同过滤的大型语言模型伦理AI提示推荐
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Ethical AI prompt recommendations in large language models using collaborative filtering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis, Nikolaos Polatidis
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确涉及伦理AI主题，这属于被明确排除的无关主题范畴。虽然提到了协同过滤和LLM，但核心焦点是伦理方面的提示推荐，与当前关注的推荐系统、搜索或广告领域的技术进展无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:03:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06924v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06924v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contributions include a synthetic dataset for prompt recommendations and the application of collaborative filtering. The work also tackles challenges in ethical AI, such as bias mitigation, transparency, and preventing unethical prompt engineering.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06888v1" target="_blank" rel="noopener noreferrer">
                M3Retrieve：医学多模态检索基准测试
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arkadeep Acharya, Akash Ghosh, Pradeepika Verma, Kitsuchart Pasupa, Sriparna Sah...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的多模态检索基准测试，属于明确的医学领域特定应用，与搜索推荐广告的核心技术无关。虽然涉及检索技术，但医学领域的应用场景和数据类型与商业推荐搜索系统存在显著差异，无法直接迁移应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:08:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06888v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06888v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve, spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications. The dataset and the baselines code are available in this github page https://github.com/AkashGhosh/M3Retrieve.
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            <a href="https://www.alphaxiv.org/abs/2510.06823v1" target="_blank" rel="noopener noreferrer">
                揭示生成式引擎中的引用漏洞
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Exposing Citation Vulnerabilities in Generative Engines
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Riku Mochizuki, Shusuke Komatsu, Souta Noguchi, Kazuto Ataka
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注生成式引擎的引用漏洞，属于幻觉、评估基准等纯NLP中心主题，与推荐系统、搜索或广告的核心技术进展无关。论文内容涉及生成模型的可靠性问题，这在当前关注点的无关主题列表中明确排除。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:47:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06823v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06823v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We analyze answers generated by generative engines (GEs) from the perspectives of citation publishers and the content-injection barrier, defined as the difficulty for attackers to manipulate answers to user prompts by placing malicious content on the web. GEs integrate two functions: web search and answer generation that cites web pages using large language models. Because anyone can publish information on the web, GEs are vulnerable to poisoning attacks. Existing studies of citation evaluation focus on how faithfully answer content reflects cited sources, leaving unexamined which web sources should be selected as citations to defend against poisoning attacks. To fill this gap, we introduce evaluation criteria that assess poisoning threats using the citation information contained in answers. Our criteria classify the publisher attributes of citations to estimate the content-injection barrier thereby revealing the threat of poisoning attacks in current GEs. We conduct experiments in political domains in Japan and the United States (U.S.) using our criteria and show that citations from official party websites (primary sources) are approximately \(25\%\)--\(45\%\) in the U.S. and \(60\%\)--\(65\%\) in Japan, indicating that U.S. political answers are at higher risk of poisoning attacks. We also find that sources with low content-injection barriers are frequently cited yet are poorly reflected in answer content. To mitigate this threat, we discuss how publishers of primary sources can increase exposure of their web content in answers and show that well-known techniques are limited by language differences.
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            <a href="https://www.alphaxiv.org/abs/2510.06805v1" target="_blank" rel="noopener noreferrer">
                PAN 2025抄袭检测任务综述
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Overview of the Plagiarism Detection Task at PAN 2025
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>André Greiner-Petter, Maik Fröbe, Jan Philip Wahle, Terry Ruas, Bela Gipp, Akiko...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于抄袭检测任务，这属于内容安全与诚信验证领域，与推荐系统、搜索或广告的核心技术进展无关。抄袭检测主要涉及文本相似度分析和内容原创性验证，不涉及LLM技术、Transformer架构改进或推荐/搜索/广告系统的核心算法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:33:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06805v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06805v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                    The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective sources. We created a novel large-scale dataset of automatically generated plagiarism using three large language models: Llama, DeepSeek-R1, and Mistral. In this task overview paper, we outline the creation of this dataset, summarize and compare the results of all participants and four baselines, and evaluate the results on the last plagiarism detection task from PAN 2015 in order to interpret the robustness of the proposed approaches. We found that the current iteration does not invite a large variety of approaches as naive semantic similarity approaches based on embedding vectors provide promising results of up to 0.8 recall and 0.5 precision. In contrast, most of these approaches underperform significantly on the 2015 dataset, indicating a lack in generalizability.
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            <a href="https://www.alphaxiv.org/abs/2510.07315v1" target="_blank" rel="noopener noreferrer">
                Vibe Checker：将代码评估与人类偏好对齐
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            Vibe Checker: Aligning Code Evaluation with Human Preference
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ming Zhong, Xiang Zhou, Ting-Yun Chang, Qingze Wang, Nan Xu, Xiance Si, Dan Garr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于代码评估和人类偏好对齐，这属于编程辅助或代码生成领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及偏好对齐概念，但应用场景是代码而非用户行为建模，没有明确的RecSys/Search/Ads应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07315v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07315v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.SE</span></div>
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                    Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check. Vibe check is tied to real-world human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking the non-functional instructions that users routinely apply. In this paper, we hypothesize that instruction following is the missing piece underlying vibe check that represents human preference in coding besides functional correctness. To quantify models' code instruction following capabilities with measurable signals, we present VeriCode, a taxonomy of 30 verifiable code instructions together with corresponding deterministic verifiers. We use the taxonomy to augment established evaluation suites, resulting in Vibe Checker, a testbed to assess both code instruction following and functional correctness. Upon evaluating 31 leading LLMs, we show that even the strongest models struggle to comply with multiple instructions and exhibit clear functional regression. Most importantly, a composite score of functional correctness and instruction following correlates the best with human preference, with the latter emerging as the primary differentiator on real-world programming tasks. Our work identifies core factors of the vibe check, providing a concrete path for benchmarking and developing models that better align with user preferences in coding.
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            <a href="https://www.alphaxiv.org/abs/2510.07293v1" target="_blank" rel="noopener noreferrer">
                AudioMarathon：面向音频大语言模型的长上下文理解与效率的综合性基准
            </a>
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            AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peize He, Zichen Wen, Yubo Wang, Yuxuan Wang, Xiaoqian Liu, Jiajie Huang, Zehui ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频领域的基准测试和评估，属于纯音频研究方向，与推荐系统、搜索或广告领域没有直接关联。虽然涉及长上下文理解和效率，但这些技术讨论仅限于音频模态，没有展示在推荐/搜索/广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:50:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07293v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07293v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">eess.AS</span></div>
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                    Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention ($O(N^2)$) and with modeling long-range temporal dependencies. Existing audio benchmarks are built mostly from short clips and do not evaluate models in realistic long context settings. To address this gap, we introduce AudioMarathon, a benchmark designed to evaluate both understanding and inference efficiency on long-form audio. AudioMarathon provides a diverse set of tasks built upon three pillars: long-context audio inputs with durations ranging from 90.0 to 300.0 seconds, which correspond to encoded sequences of 2,250 to 7,500 audio tokens, respectively, full domain coverage across speech, sound, and music, and complex reasoning that requires multi-hop inference. We evaluate state-of-the-art LALMs and observe clear performance drops as audio length grows. We also study acceleration techniques and analyze the trade-offs of token pruning and KV cache eviction. The results show large gaps across current LALMs and highlight the need for better temporal reasoning and memory-efficient architectures. We believe AudioMarathon will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.07290v1" target="_blank" rel="noopener noreferrer">
                论大型语言模型中道德自我修正的收敛性
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            On the Convergence of Moral Self-Correction in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guangliang Liu, Haitao Mao, Bochuan Cao, Zhiyu Xue, Xitong Zhang, Rongrong Wang,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM的道德对齐和自我修正，这属于伦理和价值观对齐范畴，明确属于被排除的非技术性话题。论文内容与推荐系统、搜索或广告的核心技术进展、架构改进或直接应用无关，没有任何可预见的实际应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:46:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07290v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07290v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only a general and abstract goal without specific details about potential issues in the response, LLMs must rely on their internal knowledge to improve response quality, a process referred to as intrinsic self-correction. The empirical success of intrinsic self-correction is evident in various applications, but how and why it is effective remains unknown. Focusing on moral self-correction in LLMs, we reveal a key characteristic of intrinsic self-correction: performance convergence through multi-round interactions; and provide a mechanistic analysis of this convergence behavior. Based on our experimental results and analysis, we uncover the underlying mechanism of convergence: consistently injected self-correction instructions activate moral concepts that reduce model uncertainty, leading to converged performance as the activated moral concepts stabilize over successive rounds. This paper demonstrates the strong potential of moral self-correction by showing that it exhibits a desirable property of converged performance.
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            <a href="https://www.alphaxiv.org/abs/2510.07243v1" target="_blank" rel="noopener noreferrer">
                LeMAJ（法律大语言模型作为法官）：连接法律推理与大语言模型评估
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            LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joseph Enguehard, Morgane Van Ermengem, Kate Atkinson, Sujeong Cha, Arijit Ghosh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于法律领域的LLM应用和评估，属于特定领域应用而非核心推荐系统、搜索或广告技术。论文内容涉及法律推理和LLM评估基准，这些主题与我的关注领域（推荐系统、搜索、广告及其相关LLM技术）没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:10:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07243v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07243v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Evaluating large language model (LLM) outputs in the legal domain presents unique challenges due to the complex and nuanced nature of legal analysis. Current evaluation approaches either depend on reference data, which is costly to produce, or use standardized assessment methods, both of which have significant limitations for legal applications. Although LLM-as-a-Judge has emerged as a promising evaluation technique, its reliability and effectiveness in legal contexts depend heavily on evaluation processes unique to the legal industry and how trustworthy the evaluation appears to the human legal expert. This is where existing evaluation methods currently fail and exhibit considerable variability. This paper aims to close the gap: a) we break down lengthy responses into 'Legal Data Points' (LDPs), self-contained units of information, and introduce a novel, reference-free evaluation methodology that reflects how lawyers evaluate legal answers; b) we demonstrate that our method outperforms a variety of baselines on both our proprietary dataset and an open-source dataset (LegalBench); c) we show how our method correlates more closely with human expert evaluations and helps improve inter-annotator agreement; and finally d) we open source our Legal Data Points for a subset of LegalBench used in our experiments, allowing the research community to replicate our results and advance research in this vital area of LLM evaluation on legal question-answering.
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            <a href="https://www.alphaxiv.org/abs/2510.07238v1" target="_blank" rel="noopener noreferrer">
                当基准过时：通过大语言模型事实性评估揭示的时间错位
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            When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xunyi Jiang, Dingyi Chang, Julian McAuley, Xin Xu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM事实性评估和基准时效性问题，这属于纯粹的NLP评估基准范畴，被明确列为无关主题。论文内容涉及幻觉和评估基准等纯NLP中心话题，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:06:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07238v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07238v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The rapid evolution of large language models (LLMs) and the real world has outpaced the static nature of widely used evaluation benchmarks, raising concerns about their reliability for evaluating LLM factuality. While substantial works continue to rely on the popular but old benchmarks, their temporal misalignment with real-world facts and modern LLMs, and their effects on LLM factuality evaluation remain underexplored. Therefore, in this work, we present a systematic investigation of this issue by examining five popular factuality benchmarks and eight LLMs released across different years. An up-to-date fact retrieval pipeline and three metrics are tailored to quantify benchmark aging and its impact on LLM factuality evaluation. Experimental results and analysis illustrate that a considerable portion of samples in the widely used factuality benchmarks are outdated, leading to unreliable assessments of LLM factuality. We hope our work can provide a testbed to assess the reliability of a benchmark for LLM factuality evaluation and inspire more research on the benchmark aging issue. Codes are available in https://github.com/JiangXunyi/BenchAge.
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            <a href="https://www.alphaxiv.org/abs/2510.07221v1" target="_blank" rel="noopener noreferrer">
                非洲语言自动语音识别需要多少语音数据？基尼亚卢旺达语和基库尤语数据规模扩展评估
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            How much speech data is necessary for ASR in African languages? An evaluation of data scaling in Kinyarwanda and Kikuyu
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Benjamin Akera, Evelyn Nafula, Patrick Walukagga, Gilbert Yiga, John Quinn, Erne...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动语音识别(ASR)在非洲语言中的应用，属于纯语音处理领域。虽然涉及数据规模扩展评估，但没有任何与推荐系统、搜索或广告相关的潜在应用场景，完全超出了关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:55:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07221v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07221v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The development of Automatic Speech Recognition (ASR) systems for low-resource African languages remains challenging due to limited transcribed speech data. While recent advances in large multilingual models like OpenAI's Whisper offer promising pathways for low-resource ASR development, critical questions persist regarding practical deployment requirements. This paper addresses two fundamental concerns for practitioners: determining the minimum data volumes needed for viable performance and characterizing the primary failure modes that emerge in production systems. We evaluate Whisper's performance through comprehensive experiments on two Bantu languages: systematic data scaling analysis on Kinyarwanda using training sets from 1 to 1,400 hours, and detailed error characterization on Kikuyu using 270 hours of training data. Our scaling experiments demonstrate that practical ASR performance (WER < 13\%) becomes achievable with as little as 50 hours of training data, with substantial improvements continuing through 200 hours (WER < 10\%). Complementing these volume-focused findings, our error analysis reveals that data quality issues, particularly noisy ground truth transcriptions, account for 38.6\% of high-error cases, indicating that careful data curation is as critical as data volume for robust system performance. These results provide actionable benchmarks and deployment guidance for teams developing ASR systems across similar low-resource language contexts. We release accompanying and models see https://github.com/SunbirdAI/kinyarwanda-whisper-eval
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                量化大语言模型心理测量评估中的数据污染
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Quantifying Data Contamination in Psychometric Evaluations of LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jongwook Han, Woojung Song, Jonggeun Lee, Yohan Jo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM评估中的数据污染问题，这属于纯粹的评估基准和NLP中心话题，与我的核心关注点无关。论文标题表明其研究的是心理测量评估中的技术问题，没有显示出在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:16:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07175v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07175v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent studies apply psychometric questionnaires to Large Language Models (LLMs) to assess high-level psychological constructs such as values, personality, moral foundations, and dark traits. Although prior work has raised concerns about possible data contamination from psychometric inventories, which may threaten the reliability of such evaluations, there has been no systematic attempt to quantify the extent of this contamination. To address this gap, we propose a framework to systematically measure data contamination in psychometric evaluations of LLMs, evaluating three aspects: (1) item memorization, (2) evaluation memorization, and (3) target score matching. Applying this framework to 21 models from major families and four widely used psychometric inventories, we provide evidence that popular inventories such as the Big Five Inventory (BFI-44) and Portrait Values Questionnaire (PVQ-40) exhibit strong contamination, where models not only memorize items but can also adjust their responses to achieve specific target scores.
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            <a href="https://www.alphaxiv.org/abs/2510.07173v1" target="_blank" rel="noopener noreferrer">
                NurseLLM：首个面向护理领域的专用语言模型
            </a>
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            NurseLLM: The First Specialized Language Model for Nursing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md Tawkat Islam Khondaker, Julia Harrington, Shady Shehata
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗护理领域的专用语言模型开发，属于明确的领域特定应用。这与我的关注点（推荐系统、搜索、广告中的核心进展及LLM技术）完全无关，且医疗领域被明确列为不相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:15:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07173v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07173v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.
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            <a href="https://www.alphaxiv.org/abs/2510.07169v1" target="_blank" rel="noopener noreferrer">
                更多数据还是更好数据？数学推理中数据选择与合成的关键分析
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            More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yike Zhao, Simin Guo, Ziqing Yang, Shifan Han, Dahua Lin, Fei Tan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于数学推理领域的数据选择与合成问题，这属于特定领域的数据处理技术。虽然数据质量是推荐和搜索系统中的重要因素，但该论文的数学推理焦点与RecSys/Search/Ads的核心技术栈没有直接关联，也没有涉及LLM、Transformer架构或异构数据建模等关键领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:07:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07169v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07169v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. In this work, we conduct a comprehensive analysis of open-source datasets and data synthesis techniques for mathematical reasoning, evaluating them under a unified pipeline designed to mirror training and deployment scenarios. We further distill effective data selection strategies and identify practical methods suitable for industrial applications. Our findings highlight that structuring data in more interpretable formats, or distilling from stronger models often outweighs simply scaling up data volume. This study provides actionable guidance for integrating training data to enhance LLM capabilities, supporting both cost-effective data curation and scalable model enhancement. We hope this work will inspire further research on how to balance "more data" versus "better data" for real-world reasoning tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.07083v1" target="_blank" rel="noopener noreferrer">
                所有声明都是平等的，但有些声明比其他声明更平等：LLM生成内容的重要性敏感事实性评估
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            All Claims Are Equal, but Some Claims Are More Equal Than Others: Importance-Sensitive Factuality Evaluation of LLM Generations
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Miriam Wanner, Leif Azzopardi, Paul Thomas, Soham Dan, Benjamin Van Durme, Nick ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM生成内容的事实性评估和幻觉检测，这属于纯粹的NLP评估基准范畴，与我的关注领域无关。论文没有展示在推荐系统、搜索或广告中的潜在应用，而是专注于通用的LLM评估方法学。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:40:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07083v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07083v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing methods for evaluating the factuality of large language model (LLM) responses treat all claims as equally important. This results in misleading evaluations when vital information is missing or incorrect as it receives the same weight as peripheral details, raising the question: how can we reliably detect such differences when there are errors in key information? Current approaches that measure factuality tend to be insensitive to omitted or false key information. To investigate this lack of sensitivity, we construct VITALERRORS, a benchmark of 6,733 queries with minimally altered LLM responses designed to omit or falsify key information. Using this dataset, we demonstrate the insensitivities of existing evaluation metrics to key information errors. To address this gap, we introduce VITAL, a set of metrics that provide greater sensitivity in measuring the factuality of responses by incorporating the relevance and importance of claims with respect to the query. Our analysis demonstrates that VITAL metrics more reliably detect errors in key information than previous methods. Our dataset, metrics, and analysis provide a foundation for more accurate and robust assessment of LLM factuality.
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            <a href="https://www.alphaxiv.org/abs/2510.07061v1" target="_blank" rel="noopener noreferrer">
                重新审视印度语言机器翻译与文本摘要细粒度评估中的指标可靠性
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            Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amir Hossein Yari, Kalmit Kulkarni, Ahmad Raza Khan, Fajri Koto
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于机器翻译和文本摘要的评估指标可靠性，属于纯粹的NLP评估基准研究。论文内容涉及特定语言（印度语言）的评估方法，与推荐系统、搜索或广告的核心技术进展、LLM赋能技术或Transformer架构改进均无直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:27:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07061v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07061v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English and other high-resource languages. This narrow focus leaves Indian languages, spoken by over 1.5 billion people, largely overlooked, casting doubt on the universality of current evaluation practices. To address this gap, we introduce ITEM, a large-scale benchmark that systematically evaluates the alignment of 26 automatic metrics with human judgments across six major Indian languages, enriched with fine-grained annotations. Our extensive evaluation, covering agreement with human judgments, sensitivity to outliers, language-specific reliability, inter-metric correlations, and resilience to controlled perturbations, reveals four central findings: (1) LLM-based evaluators show the strongest alignment with human judgments at both segment and system levels; (2) outliers exert a significant impact on metric-human agreement; (3) in TS, metrics are more effective at capturing content fidelity, whereas in MT, they better reflect fluency; and (4) metrics differ in their robustness and sensitivity when subjected to diverse perturbations. Collectively, these findings offer critical guidance for advancing metric design and evaluation in Indian languages.
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            <a href="https://www.alphaxiv.org/abs/2510.07060v1" target="_blank" rel="noopener noreferrer">
                地方新闻是否保持本地化？：辛克莱收购电视台后的在线内容转变
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            Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Miriam Wanner, Sophia Hager, Anjalie Field
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究媒体收购对地方新闻内容的影响，属于媒体研究和内容分析领域。虽然涉及在线内容，但与推荐系统、搜索或广告的核心技术、LLM应用或Transformer架构无关，也不涉及异构数据统一建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:27:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07060v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.
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            <a href="https://www.alphaxiv.org/abs/2510.06975v1" target="_blank" rel="noopener noreferrer">
                VelLMes：一种基于人工智能的高交互性欺骗框架
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            VelLMes: A high-interaction AI-based deception framework
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muris Sladić, Veronica Valeros, Carlos Catania, Sebastian Garcia
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及欺骗框架和AI安全领域，这属于被明确排除的无关主题（安全、隐私等）。该研究专注于对抗性攻击和防御机制，与推荐系统、搜索、广告的核心技术进展或使能技术没有任何关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:00:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06975v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06975v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service, mainly SSH shells. These systems - but also the deception technologies not based on LLMs - lack an extensive evaluation that includes human attackers. Generative AI has recently become a valuable asset for cybersecurity researchers and practitioners, and the field of cyber-deception is no exception. Researchers have demonstrated how LLMs can be leveraged to create realistic-looking honeytokens, fake users, and even simulated systems that can be used as honeypots. This paper presents an AI-based deception framework called VelLMes, which can simulate multiple protocols and services such as SSH Linux shell, MySQL, POP3, and HTTP. All of these can be deployed and used as honeypots, thus VelLMes offers a variety of choices for deception design based on the users' needs. VelLMes is designed to be attacked by humans, so interactivity and realism are key for its performance. We evaluate the generative capabilities and the deception capabilities. Generative capabilities were evaluated using unit tests for LLMs. The results of the unit tests show that, with careful prompting, LLMs can produce realistic-looking responses, with some LLMs having a 100% passing rate. In the case of the SSH Linux shell, we evaluated deception capabilities with 89 human attackers. The results showed that about 30% of the attackers thought that they were interacting with a real system when they were assigned an LLM-based honeypot. Lastly, we deployed 10 instances of the SSH Linux shell honeypot on the Internet to capture real-life attacks. Analysis of these attacks showed us that LLM honeypots simulating Linux shells can perform well against unstructured and unexpected attacks on the Internet, responding correctly to most of the issued commands.
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            <a href="https://www.alphaxiv.org/abs/2510.06974v1" target="_blank" rel="noopener noreferrer">
                使用性别代词和社会群体探测中文大语言模型中的社会身份偏见
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Geng Liu, Feng Li, Junjie Mu, Mengxiao Zhu, Francesco Pierri
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM偏见探测和评估，这属于被明确排除的'公平性、伦理或其他非技术性话题'范畴。虽然涉及中文LLMs，但核心关注点是社会身份偏见而非技术进展或应用，与推荐系统、搜索或广告的技术焦点无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:00:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06974v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06974v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns about their potential to reflect and amplify social biases. We investigate social identity framing in Chinese LLMs using Mandarin-specific prompts across ten representative Chinese LLMs, evaluating responses to ingroup ("We") and outgroup ("They") framings, and extending the setting to 240 social groups salient in the Chinese context. To complement controlled experiments, we further analyze Chinese-language conversations from a corpus of real interactions between users and chatbots. Across models, we observe systematic ingroup-positive and outgroup-negative tendencies, which are not confined to synthetic prompts but also appear in naturalistic dialogue, indicating that bias dynamics might strengthen in real interactions. Our study provides a language-aware evaluation framework for Chinese LLMs, demonstrating that social identity biases documented in English generalize cross-linguistically and intensify in user-facing contexts.
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            <a href="https://www.alphaxiv.org/abs/2510.06965v1" target="_blank" rel="noopener noreferrer">
                EDUMATH：生成符合课程标准的教育数学应用题
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            EDUMATH: Generating Standards-aligned Educational Math Word Problems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bryan R. Christ, Penelope Molitz, Jonathan Kropko, Thomas Hartvigsen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于教育领域数学题目的生成，属于纯粹的AIGC和内容生成应用，与推荐系统、搜索或广告的核心技术进展无关。论文内容不涉及任何推荐、搜索或广告领域的潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:53:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06965v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06965v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students' interests and ability levels can increase learning outcomes. However, teachers struggle to find time to customize MWPs for each student given large class sizes and increasing burnout. We propose that LLMs can support math education by generating MWPs customized to student interests and math education standards. To this end, we use a joint human expert-LLM judge approach to evaluate over 11,000 MWPs generated by open and closed LLMs and develop the first teacher-annotated dataset for standards-aligned educational MWP generation. We show the value of our data by using it to train a 12B open model that matches the performance of larger and more capable open models. We also use our teacher-annotated data to train a text classifier that enables a 30B open LLM to outperform existing closed baselines without any training. Next, we show our models' MWPs are more similar to human-written MWPs than those from existing models. We conclude by conducting the first study of customized LLM-generated MWPs with grade school students, finding they perform similarly on our models' MWPs relative to human-written MWPs but consistently prefer our customized MWPs.
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            <a href="https://www.alphaxiv.org/abs/2510.06961v1" target="_blank" rel="noopener noreferrer">
                开放ASR排行榜：迈向可复现和透明的多语言及长语音识别评估
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            Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vaibhav Srivastav, Steven Zheng, Eric Bezzam, Eustache Le Bihan, Nithin Koluguri...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音识别（ASR）的评估基准和排行榜，属于纯粹的语音处理领域。虽然提到了多语言和长语音识别，但这些技术没有明确的推荐系统、搜索或广告应用场景，完全超出了指定的关注范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:44:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06961v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06961v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.SD</span><span class="category-tag">eess.AS</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite rapid progress, ASR evaluation remains saturated with short-form English, and efficiency is rarely reported. We present the Open ASR Leaderboard, a fully reproducible benchmark and interactive leaderboard comparing 60+ open-source and proprietary systems across 11 datasets, including dedicated multilingual and long-form tracks. We standardize text normalization and report both word error rate (WER) and inverse real-time factor (RTFx), enabling fair accuracy-efficiency comparisons. For English transcription, Conformer encoders paired with LLM decoders achieve the best average WER but are slower, while CTC and TDT decoders deliver much better RTFx, making them attractive for long-form and offline use. Whisper-derived encoders fine-tuned for English improve accuracy but often trade off multilingual coverage. All code and dataset loaders are open-sourced to support transparent, extensible evaluation.
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            <a href="https://www.alphaxiv.org/abs/2510.06841v1" target="_blank" rel="noopener noreferrer">
                GAMBIT+：用于评估机器翻译质量评估指标中性别偏见的挑战集
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            GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metrics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Giorgos Filandrianos, Orfeas Menis Mastromichalakis, Wafaa Mohammed, Giuseppe At...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器翻译质量评估中的性别偏见评估，这属于公平性、伦理和偏见检测范畴，明确属于用户指定的不相关主题。该研究没有涉及推荐系统、搜索或广告的核心技术进展，也没有展示在LLM、Transformer架构或异构数据建模方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:09:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06841v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06841v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Gender bias in machine translation (MT) systems has been extensively documented, but bias in automatic quality estimation (QE) metrics remains comparatively underexplored. Existing studies suggest that QE metrics can also exhibit gender bias, yet most analyses are limited by small datasets, narrow occupational coverage, and restricted language variety. To address this gap, we introduce a large-scale challenge set specifically designed to probe the behavior of QE metrics when evaluating translations containing gender-ambiguous occupational terms. Building on the GAMBIT corpus of English texts with gender-ambiguous occupations, we extend coverage to three source languages that are genderless or natural-gendered, and eleven target languages with grammatical gender, resulting in 33 source-target language pairs. Each source text is paired with two target versions differing only in the grammatical gender of the occupational term(s) (masculine vs. feminine), with all dependent grammatical elements adjusted accordingly. An unbiased QE metric should assign equal or near-equal scores to both versions. The dataset's scale, breadth, and fully parallel design, where the same set of texts is aligned across all languages, enables fine-grained bias analysis by occupation and systematic comparisons across languages.
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            <a href="https://www.alphaxiv.org/abs/2510.06811v1" target="_blank" rel="noopener noreferrer">
                BlackboxNLP-2025 MIB共享任务：探索电路定位方法的集成策略
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            BlackboxNLP-2025 MIB Shared Task: Exploring Ensemble Strategies for Circuit Localization Methods
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Philipp Mondorf, Mingyang Wang, Sebastian Gerstner, Ahmad Dawar Hakimi, Yihong L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于NLP模型可解释性领域的电路定位方法集成策略，属于纯粹的NLP可解释性研究。虽然涉及模型内部机制分析，但主要关注黑盒NLP的基准测试任务，与推荐系统、搜索或广告的核心技术进展没有直接关联，也不涉及Transformer架构改进或LLM在推荐/搜索中的直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:39:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06811v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06811v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Circuit Localization track of the Mechanistic Interpretability Benchmark (MIB) evaluates methods for localizing circuits within large language models (LLMs), i.e., subnetworks responsible for specific task behaviors. In this work, we investigate whether ensembling two or more circuit localization methods can improve performance. We explore two variants: parallel and sequential ensembling. In parallel ensembling, we combine attribution scores assigned to each edge by different methods-e.g., by averaging or taking the minimum or maximum value. In the sequential ensemble, we use edge attribution scores obtained via EAP-IG as a warm start for a more expensive but more precise circuit identification method, namely edge pruning. We observe that both approaches yield notable gains on the benchmark metrics, leading to a more precise circuit identification approach. Finally, we find that taking a parallel ensemble over various methods, including the sequential ensemble, achieves the best results. We evaluate our approach in the BlackboxNLP 2025 MIB Shared Task, comparing ensemble scores to official baselines across multiple model-task combinations.
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            <a href="https://www.alphaxiv.org/abs/2510.06800v1" target="_blank" rel="noopener noreferrer">
                FURINA：通过可扩展多智能体协作流程实现完全可定制的角色扮演基准
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            FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haotian Wu, Shufan Jiang, Chios Chen, Yiyang Feng, Hehai Lin, Heqing Zou, Yao Sh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于角色扮演基准和多智能体协作，属于对话系统和智能体研究领域。虽然涉及多智能体技术，但缺乏与推荐系统、搜索或广告的直接关联，也没有明确的Transformer架构创新或LLM基础技术进步。该基准主要面向角色扮演评估，不涉及用户行为建模、内容排序或广告投放等核心关注领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:30:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06800v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06800v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.MA</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limited adaptability across diverse application scenarios. To address this gap, we introduce FURINA-Builder, a novel multi-agent collaboration pipeline that automatically constructs fully customizable RP benchmarks at any scale. It enables evaluation of arbitrary characters across diverse scenarios and prompt formats, as the first benchmark builder in RP area for adaptable assessment. FURINA-Builder simulates dialogues between a test character and other characters drawn from a well-constructed character-scene pool, while an LLM judge selects fine-grained evaluation dimensions and adjusts the test character's responses into final test utterances. Using this pipeline, we build FURINA-Bench, a new comprehensive role-playing benchmark featuring both established and synthesized test characters, each assessed with dimension-specific evaluation criteria. Human evaluation and preliminary separability analysis justify our pipeline and benchmark design. We conduct extensive evaluations of cutting-edge LLMs and find that o3 and DeepSeek-R1 achieve the best performance on English and Chinese RP tasks, respectively. Across all models, established characters consistently outperform synthesized ones, with reasoning capabilities further amplifying this disparity. Interestingly, we observe that model scale does not monotonically reduce hallucinations. More critically, for reasoning LLMs, we uncover a novel trade-off: reasoning improves RP performance but simultaneously increases RP hallucinations. This trade-off extends to a broader Pareto frontier between RP performance and reliability for all LLMs. These findings demonstrate the effectiveness of FURINA-Builder and the challenge posed by FURINA-Bench.
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            <a href="https://www.alphaxiv.org/abs/2510.06749v1" target="_blank" rel="noopener noreferrer">
                基于流畅度的多参考评估在语法错误纠正中的形式化框架
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            A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Eitan Klinger, Zihao Huang, Tran Minh Nguyen, Emma Jayeon Park, Yige Chen, Yang ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语法错误纠正的评估方法，属于纯粹的NLP评估基准研究。虽然评估是重要环节，但该工作没有涉及推荐系统、搜索或广告的核心技术，也没有展示在Transformer架构、LLM技术或异构数据处理方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:15:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06749v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06749v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Evaluating grammatical error correction requires metrics that reflect the diversity of valid human corrections rather than privileging a single reference. Existing frameworks, largely edit-based and English-centric, rely on rigid alignments between system and reference edits, limiting their applicability in multilingual and generative settings. This paper introduces a formal framework for \textit{fluency-based multi-reference evaluation}, framing $n$-gram similarity as an aggregation problem over multiple legitimate corrections. Within this formulation, we instantiate GLEU through four aggregation strategies--\textsc{select-best}, \textsc{simple-average}, \textsc{weighted-average}, and \textsc{merged-counts}--and analyze their properties of boundedness, monotonicity, and sensitivity to reference variation. Empirical results on Czech, Estonian, Ukrainian, and Chinese corpora show that these strategies capture complementary aspects of fluency and coverage. The framework unifies multi-reference evaluation into a principled, fluency-oriented approach that incorporates linguistic diversity without penalizing legitimate variation.
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            <a href="https://www.alphaxiv.org/abs/2510.06743v1" target="_blank" rel="noopener noreferrer">
                评估大语言模型在历史文档光学字符识别中的应用：面向数字人文的方法论框架
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        <div class="mb-2 text-base text-gray-700">
            Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maria Levchenko
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于历史文档OCR和数字人文领域的LLM评估，属于特定领域应用而非核心推荐系统、搜索或广告技术。论文内容涉及文档处理和人文学科应用，与我的关注点（RecSys/Search/Ads核心进展、使能技术及其应用）没有直接关联，属于明确的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:01:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06743v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06743v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">68T50</span></div>
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                    Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, addressing contamination risks and systematic biases in diplomatic transcription. Using 18th-century Russian Civil font texts, we introduce novel metrics including Historical Character Preservation Rate (HCPR) and Archaic Insertion Rate (AIR), alongside protocols for contamination control and stability testing. We evaluate 12 multimodal LLMs, finding that Gemini and Qwen models outperform traditional OCR while exhibiting over-historicization: inserting archaic characters from incorrect historical periods. Post-OCR correction degrades rather than improves performance. Our methodology provides digital humanities practitioners with guidelines for model selection and quality assessment in historical corpus digitization.
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            <a href="https://www.alphaxiv.org/abs/2510.06738v1" target="_blank" rel="noopener noreferrer">
                AWM：面向大型语言模型的精确权重矩阵指纹技术
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            AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Boyi Zeng, Lin Chen, Ziwei He, Xinbing Wang, Zhouhan Lin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及指纹技术，这属于明确排除的无关主题。虽然提到了大型语言模型，但核心焦点是权重矩阵的指纹识别，这与推荐系统、搜索或广告中的核心技术进展、LLM应用或Transformer架构改进无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 07:51:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06738v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06738v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training processes that models typically undergo-such as supervised fine-tuning, extensive continued pretraining, reinforcement learning, multi-modal extension, pruning, and upcycling-pose significant challenges to reliable identification. In this work, we propose a training-free fingerprinting method based on weight matrices. We leverage the Linear Assignment Problem (LAP) and an unbiased Centered Kernel Alignment (CKA) similarity to neutralize the effects of parameter manipulations, yielding a highly robust and high-fidelity similarity metric. On a comprehensive testbed of 60 positive and 90 negative model pairs, our method demonstrates exceptional robustness against all six aforementioned post-training categories while exhibiting a near-zero risk of false positives. By achieving perfect scores on all classification metrics, our approach establishes a strong basis for reliable model lineage verification. Moreover, the entire computation completes within 30s on an NVIDIA 3090 GPU. The code is available at https://github.com/LUMIA-Group/AWM.
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            <a href="https://www.alphaxiv.org/abs/2510.06706v1" target="_blank" rel="noopener noreferrer">
                XLSR-Kanformer：一种集成KAN的合成语音检测模型
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            XLSR-Kanformer: A KAN-Intergrated model for Synthetic Speech Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Phuong Tuan Dat, Tran Huy Dat
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于合成语音检测，属于语音处理领域，与推荐系统、搜索或广告的核心技术无关。论文标题中提到的KAN集成和XLSR架构没有显示出在RecSys/Search/Ads领域的潜在应用价值，完全超出了关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:58:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06706v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06706v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CL</span><span class="category-tag">eess.AS</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in speech synthesis technologies have led to increasingly sophisticated spoofing attacks, posing significant challenges for automatic speaker verification systems. While systems based on self-supervised learning (SSL) models, particularly the XLSR-Conformer architecture, have demonstrated remarkable performance in synthetic speech detection, there remains room for architectural improvements. In this paper, we propose a novel approach that replaces the traditional Multi-Layer Perceptron (MLP) in the XLSR-Conformer model with a Kolmogorov-Arnold Network (KAN), a powerful universal approximator based on the Kolmogorov-Arnold representation theorem. Our experimental results on ASVspoof2021 demonstrate that the integration of KAN to XLSR-Conformer model can improve the performance by 60.55% relatively in Equal Error Rate (EER) LA and DF sets, further achieving 0.70% EER on the 21LA set. Besides, the proposed replacement is also robust to various SSL architectures. These findings suggest that incorporating KAN into SSL-based models is a promising direction for advances in synthetic speech detection.
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                字里行间：基于零阶梯度估计的可靠黑盒大语言模型指纹识别
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            Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuo Shao, Yiming Li, Hongwei Yao, Yifei Chen, Yuchen Yang, Zhan Qin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及指纹识别技术，这属于明确的无关主题范畴。虽然提到了LLM技术，但其核心应用方向（指纹识别）与搜索、推荐、广告等核心领域无关，且指纹识别被明确列为应排除的非技术性主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:27:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06605v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06605v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The substantial investment required to develop Large Language Models (LLMs) makes them valuable intellectual property, raising significant concerns about copyright protection. LLM fingerprinting has emerged as a key technique to address this, which aims to verify a model's origin by extracting an intrinsic, unique signature (a "fingerprint") and comparing it to that of a source model to identify illicit copies. However, existing black-box fingerprinting methods often fail to generate distinctive LLM fingerprints. This ineffectiveness arises because black-box methods typically rely on model outputs, which lose critical information about the model's unique parameters due to the usage of non-linear functions. To address this, we first leverage Fisher Information Theory to formally demonstrate that the gradient of the model's input is a more informative feature for fingerprinting than the output. Based on this insight, we propose ZeroPrint, a novel method that approximates these information-rich gradients in a black-box setting using zeroth-order estimation. ZeroPrint overcomes the challenge of applying this to discrete text by simulating input perturbations via semantic-preserving word substitutions. This operation allows ZeroPrint to estimate the model's Jacobian matrix as a unique fingerprint. Experiments on the standard benchmark show ZeroPrint achieves a state-of-the-art effectiveness and robustness, significantly outperforming existing black-box methods.
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            <a href="https://www.alphaxiv.org/abs/2510.06557v1" target="_blank" rel="noopener noreferrer">
                马尔可夫思考者
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            The Markovian Thinker
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Milad Aghajohari, Kamran Chitsaz, Amirhossein Kazemnejad, Sarath Chandar, Alessa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">标题'The Markovian Thinker'暗示可能涉及马尔可夫过程或序列建模，但缺乏任何与推荐系统、搜索、广告或LLM技术的具体关联。没有明确的技术领域或应用场景表明与当前关注点相关，因此评分极低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 01:18:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06557v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06557v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Yet the standard RL "thinking environment", where the state is the prompt plus all prior reasoning tokens, makes the state unbounded and forces attention-based policies to pay quadratic compute as thoughts lengthen. We revisit the environment itself. We propose Markovian Thinking, a paradigm in which the policy advances reasoning while conditioning on a constant-size state, decoupling thinking length from context size. As an immediate consequence this yields linear compute with constant memory. We instantiate this idea with Delethink, an RL environment that structures reasoning into fixed-size chunks. Within each chunk, the model thinks as usual; at the boundary, the environment resets the context and reinitializes the prompt with a short carryover. Through RL, the policy learns to write a textual state near the end of each chunk sufficient for seamless continuation of reasoning after reset. Trained in this environment, an R1-Distill 1.5B model reasons in 8K-token chunks yet thinks up to 24K tokens, matching or surpassing LongCoT-RL trained with a 24K budget. With test-time scaling, Delethink continues to improve where LongCoT plateaus. The effect of linear compute is substantial: we empirically estimate at 96K average thinking length LongCoT-RL costs 27 H100-months vs. 7 for Delethink. Analysis at RL initialization shows off-the-shelf reasoning models (1.5B-120B) often sample Markovian traces zero-shot across diverse benchmarks, providing positive samples that make RL effective at scale. Our results show that redesigning the thinking environment is a powerful lever: it enables very long reasoning without quadratic overhead and opens a path toward efficient, scalable reasoning LLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.07319v1" target="_blank" rel="noopener noreferrer">
                时序提示至关重要：重新思考指代视频目标分割
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Temporal Prompting Matters: Rethinking Referring Video Object Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ci-Siang Lin, Min-Hung Chen, I-Jieh Liu, Chien-Yi Wang, Sifei Liu, Yu-Chiang Fra...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的视频目标分割任务，特别是涉及语言指代的分割方法。虽然提到了时序建模，但其核心是纯粹的视觉-语言理解问题，与推荐系统、搜索或广告中的排序、用户建模等核心任务没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明显应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07319v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07319v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense mask annotations, which could be computation-consuming and less scalable. In this work, we rethink the RVOS problem and aim to investigate the key to this task. Based on existing foundation segmentation models, we decompose the RVOS task into referring, video, and segmentation factors, and propose a Temporal Prompt Generation and Selection (Tenet) framework to address the referring and video factors while leaving the segmentation problem to foundation models. To efficiently adapt image-based foundation segmentation models to referring video object segmentation, we leverage off-the-shelf object detectors and trackers to produce temporal prompts associated with the referring sentence. While high-quality temporal prompts could be produced, they can not be easily identified from confidence scores. To tackle this issue, we propose Prompt Preference Learning to evaluate the quality of the produced temporal prompts. By taking such prompts to instruct image-based foundation segmentation models, we would be able to produce high-quality masks for the referred object, enabling efficient model adaptation to referring video object segmentation. Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet framework.
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            <a href="https://www.alphaxiv.org/abs/2510.07313v1" target="_blank" rel="noopener noreferrer">
                WristWorld：通过4D世界模型生成腕部视图用于机器人操作
            </a>
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        <div class="mb-2 text-base text-gray-700">
            WristWorld: Generating Wrist-Views via 4D World Models for Robotic Manipulation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zezhong Qian, Xiaowei Chi, Yuming Li, Shizun Wang, Zhiyuan Qin, Xiaozhu Ju, Siru...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人操作和4D世界模型生成腕部视图，属于纯粹的机器人视觉领域。虽然标题提到'生成'，但这与推荐系统、搜索或广告的核心技术无关，也没有任何潜在的Transformer架构或LLM应用关联。该研究属于纯粹的机器人视觉应用，完全超出关注范围。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:59:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07313v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07313v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Wrist-view observations are crucial for VLA models as they capture fine-grained hand-object interactions that directly enhance manipulation performance. Yet large-scale datasets rarely include such recordings, resulting in a substantial gap between abundant anchor views and scarce wrist views. Existing world models cannot bridge this gap, as they require a wrist-view first frame and thus fail to generate wrist-view videos from anchor views alone. Amid this gap, recent visual geometry models such as VGGT emerge with geometric and cross-view priors that make it possible to address extreme viewpoint shifts. Inspired by these insights, we propose WristWorld, the first 4D world model that generates wrist-view videos solely from anchor views. WristWorld operates in two stages: (i) Reconstruction, which extends VGGT and incorporates our Spatial Projection Consistency (SPC) Loss to estimate geometrically consistent wrist-view poses and 4D point clouds; (ii) Generation, which employs our video generation model to synthesize temporally coherent wrist-view videos from the reconstructed perspective. Experiments on Droid, Calvin, and Franka Panda demonstrate state-of-the-art video generation with superior spatial consistency, while also improving VLA performance, raising the average task completion length on Calvin by 3.81% and closing 42.4% of the anchor-wrist view gap.
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            <a href="https://www.alphaxiv.org/abs/2510.07302v1" target="_blank" rel="noopener noreferrer">
                SpecGuard：基于谱投影的先进隐形水印技术
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        <div class="mb-2 text-base text-gray-700">
            SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Inzamamul Alam, Md Tanvir Islam, Khan Muhammad, Simon S. Woo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及数字水印技术，属于安全保护领域，与我的核心关注点（推荐系统、搜索、广告及相关的LLM/Transformer技术）完全无关。水印技术主要用于内容认证和版权保护，属于被明确排除的隐私/安全相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:56:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07302v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07302v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spatial domain to the frequency domain using spectral projection of a higher frequency band that is decomposed by wavelet projection. Spectral projection employs Fast Fourier Transform approximation to transform spatial data into the frequency domain efficiently. In the encoding phase, a strength factor enhances resilience against diverse attacks, including adversarial, geometric, and regeneration-based distortions, ensuring the preservation of copyrighted information. Meanwhile, the decoder leverages Parseval's theorem to effectively learn and extract the watermark pattern, enabling accurate retrieval under challenging transformations. We evaluate the proposed SpecGuard based on the embedded watermark's invisibility, capacity, and robustness. Comprehensive experiments demonstrate the proposed SpecGuard outperforms the state-of-the-art models. To ensure reproducibility, the full code is released on \href{https://github.com/inzamamulDU/SpecGuard_ICCV_2025}{\textcolor{blue}{\textbf{GitHub}}}.
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            <a href="https://www.alphaxiv.org/abs/2510.07277v1" target="_blank" rel="noopener noreferrer">
                评估用于糖尿病性黄斑水肿检测的眼底特异性基础模型
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        <div class="mb-2 text-base text-gray-700">
            Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Franco Javier Arellano, José Ignacio Orlando
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的眼底图像分析和糖尿病性黄斑水肿检测，属于明确的医疗应用范畴。根据用户指定的无关主题，医学、生物学等特定领域应用应被排除，且该论文与推荐系统、搜索、广告或相关使能技术无任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:41:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07277v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07277v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative solution. However, it is unclear if they can cope with DME detection in particular. In this paper, we systematically compare different FM and standard transfer learning approaches for this task. Specifically, we compare the two most popular FM for retinal images--RETFound and FLAIR--and an EfficientNet-B0 backbone, across different training regimes and evaluation settings in IDRiD, MESSIDOR-2 and OCT-and-Eye-Fundus-Images (OEFI). Results show that despite their scale, FM do not consistently outperform fine-tuned CNNs in this task. In particular, an EfficientNet-B0 ranked first or second in terms of area under the ROC and precision/recall curves in most evaluation settings, with RETFound only showing promising results in OEFI. FLAIR, on the other hand, demonstrated competitive zero-shot performance, achieving notable AUC-PR scores when prompted appropriately. These findings reveal that FM might not be a good tool for fine-grained ophthalmic tasks such as DME detection even after fine-tuning, suggesting that lightweight CNNs remain strong baselines in data-scarce environments.
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            <a href="https://www.alphaxiv.org/abs/2510.07249v1" target="_blank" rel="noopener noreferrer">
                TalkCuts：用于多镜头人类语音视频生成的大规模数据集
            </a>
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        <div class="mb-2 text-base text-gray-700">
            TalkCuts: A Large-Scale Dataset for Multi-Shot Human Speech Video Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaben Chen, Zixin Wang, Ailing Zeng, Yang Fu, Xueyang Yu, Siyuan Cen, Julian Ta...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人类语音视频生成，属于纯粹的视觉和语音生成领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然视频生成技术可能有潜在的广告创意应用，但这属于被明确排除的'非排序广告话题'范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 17:16:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07249v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07249v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this work, we present TalkCuts, a large-scale dataset designed to facilitate the study of multi-shot human speech video generation. Unlike existing datasets that focus on single-shot, static viewpoints, TalkCuts offers 164k clips totaling over 500 hours of high-quality human speech videos with diverse camera shots, including close-up, half-body, and full-body views. The dataset includes detailed textual descriptions, 2D keypoints and 3D SMPL-X motion annotations, covering over 10k identities, enabling multimodal learning and evaluation. As a first attempt to showcase the value of the dataset, we present Orator, an LLM-guided multi-modal generation framework as a simple baseline, where the language model functions as a multi-faceted director, orchestrating detailed specifications for camera transitions, speaker gesticulations, and vocal modulation. This architecture enables the synthesis of coherent long-form videos through our integrated multi-modal video generation module. Extensive experiments in both pose-guided and audio-driven settings show that training on TalkCuts significantly enhances the cinematographic coherence and visual appeal of generated multi-shot speech videos. We believe TalkCuts provides a strong foundation for future work in controllable, multi-shot speech video generation and broader multimodal learning.
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            <a href="https://www.alphaxiv.org/abs/2510.07217v1" target="_blank" rel="noopener noreferrer">
                GenPilot：一种用于图像生成中测试时提示优化的多智能体系统
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            GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Z...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像生成的提示优化，属于纯粹的AIGC和内容生成领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了多智能体系统，但应用场景仅限于图像生成，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:51:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07217v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07217v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods operate on fixed prompts and on noise or sample numbers, limiting their interpretability and adaptability. To solve these, we introduce a flexible and efficient test-time prompt optimization strategy that operates directly on the input text. We propose a plug-and-play multi-agent system called GenPilot, integrating error analysis, clustering-based adaptive exploration, fine-grained verification, and a memory module for iterative optimization. Our approach is model-agnostic, interpretable, and well-suited for handling long and complex prompts. Simultaneously, we summarize the common patterns of errors and the refinement strategy, offering more experience and encouraging further exploration. Experiments on DPG-bench and Geneval with improvements of up to 16.9% and 5.7% demonstrate the strong capability of our methods in enhancing the text and image consistency and structural coherence of generated images, revealing the effectiveness of our test-time prompt optimization strategy. The code is available at https://github.com/27yw/GenPilot.
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            <a href="https://www.alphaxiv.org/abs/2510.07191v1" target="_blank" rel="noopener noreferrer">
                分辨率缩放决定DINOv3在胸部X光片分类中的迁移性能
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            Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl, Jakob Nikolas Kather, Sve...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（胸部X光片）分类，这属于明确的无关主题范畴。虽然提到了DINOv3模型，但其应用场景是医学领域，与推荐系统、搜索或广告没有任何直接或潜在关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:25:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07191v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07191v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Self-supervised learning (SSL) has advanced visual representation learning, but its value in chest radiography, a high-volume imaging modality with fine-grained findings, remains unclear. Meta's DINOv3 extends earlier SSL models through Gram-anchored self-distillation. Whether these design choices improve transfer learning for chest radiography has not been systematically tested. We benchmarked DINOv3 against DINOv2 and ImageNet initialization across seven datasets (n>814,000). Two representative backbones were evaluated: ViT-B/16 and ConvNeXt-B. Images were analyzed at 224x224, 512x512, and 1024x1024 pixels. We additionally assessed frozen features from a 7B model. The primary outcome was mean AUROC across labels. At 224x224, DINOv3 and DINOv2 achieved comparable performance on adult datasets. Increasing resolution to 512x512 yielded consistent improvements for DINOv3 over both DINOv2 and ImageNet. In contrast, results in pediatric cohort showed no differences across initializations. Across all settings, ConvNeXt-B outperformed ViT-B/16. Models using frozen DINOv3-7B features underperformed relative to fully finetuned 86-89M-parameter backbones, highlighting the importance of domain adaptation. Scaling to 1024x1024 did not further improve accuracy. Resolution-related gains were most evident for boundary-dependent and small focal abnormalities. In chest radiography, higher input resolution is critical for leveraging the benefits of modern self-supervised models. 512x512 pixels represent a practical upper limit where DINOv3-initialized ConvNeXt-B networks provide the strongest performance, while larger inputs offer minimal return on cost. Clinically, these findings support use of finetuned, mid-sized backbones at 512x512 for chest radiograph interpretation, with the greatest gains expected in detecting subtle or boundary-centered lesions relevant to emergency and critical care settings.
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            <a href="https://www.alphaxiv.org/abs/2510.07190v1" target="_blank" rel="noopener noreferrer">
                MV-Performer：驯服视频扩散模型以实现忠实且同步的多视角表演者合成
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        <div class="mb-2 text-base text-gray-700">
            MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yihao Zhi, Chenghong Li, Hongjie Liao, Xihe Yang, Zhengwentai Sun, Jiahao Chang,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成和多视角合成，属于纯粹的视觉内容生成领域。虽然涉及扩散模型技术，但主要应用于表演者视频合成，与推荐系统、搜索或广告的核心技术需求没有直接关联，也不具备在这些领域的明显应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:24:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07190v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07190v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily concentrate on redirecting camera trajectory within the front view while struggling to generate 360-degree viewpoint changes. In this paper, we focus on human-centric subdomain and present MV-Performer, an innovative framework for creating synchronized novel view videos from monocular full-body captures. To achieve a 360-degree synthesis, we extensively leverage the MVHumanNet dataset and incorporate an informative condition signal. Specifically, we use the camera-dependent normal maps rendered from oriented partial point clouds, which effectively alleviate the ambiguity between seen and unseen observations. To maintain synchronization in the generated videos, we propose a multi-view human-centric video diffusion model that fuses information from the reference video, partial rendering, and different viewpoints. Additionally, we provide a robust inference procedure for in-the-wild video cases, which greatly mitigates the artifacts induced by imperfect monocular depth estimation. Extensive experiments on three datasets demonstrate our MV-Performer's state-of-the-art effectiveness and robustness, setting a strong model for human-centric 4D novel view synthesis.
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            <a href="https://www.alphaxiv.org/abs/2510.07181v1" target="_blank" rel="noopener noreferrer">
                TIGeR：用于机器人的视觉语言模型中的工具集成几何推理
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            TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yi Han, Cheng Chi, Enshen Zhou, Shanyu Rong, Jingkun An, Pengwei Wang, Zhongyuan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人领域的视觉语言模型和几何推理，与推荐系统、搜索或广告的核心领域进展、使能技术或直接应用没有明显关联。虽然提到了视觉语言模型，但其应用场景是机器人而非异构数据建模，无法看出在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 16:20:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07181v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Vision-Language Models (VLMs) have shown remarkable capabilities in spatial reasoning, yet they remain fundamentally limited to qualitative precision and lack the computational precision required for real-world robotics. Current approaches fail to leverage metric cues from depth sensors and camera calibration, instead reducing geometric problems to pattern recognition tasks that cannot deliver the centimeter-level accuracy essential for robotic manipulation. We present TIGeR (Tool-Integrated Geometric Reasoning), a novel framework that transforms VLMs from perceptual estimators to geometric computers by enabling them to generate and execute precise geometric computations through external tools. Rather than attempting to internalize complex geometric operations within neural networks, TIGeR empowers models to recognize geometric reasoning requirements, synthesize appropriate computational code, and invoke specialized libraries for exact calculations. To support this paradigm, we introduce TIGeR-300K, a comprehensive tool-invocation-oriented dataset covering point transformations, pose estimation, trajectory generation, and spatial compatibility verification, complete with tool invocation sequences and intermediate computations. Through a two-stage training pipeline combining supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) with our proposed hierarchical reward design, TIGeR achieves SOTA performance on geometric reasoning benchmarks while demonstrating centimeter-level precision in real-world robotic manipulation tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.07129v1" target="_blank" rel="noopener noreferrer">
                基于图条件扩散的可控病理图像生成
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        <div class="mb-2 text-base text-gray-700">
            Graph Conditioned Diffusion for Controllable Histopathology Image Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sarah Cechnicka, Matthew Baugh, Weitong Zhang, Mischa Dombrowski, Zhe Li, Johann...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于病理图像生成，属于医学影像领域，与我的关注领域（推荐系统、搜索、广告）完全无关。扩散模型在图像生成方面的技术本身具有价值，但该论文的应用场景是特定的医疗领域病理图像，没有显示出在推荐、搜索或广告系统中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:26:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07129v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07129v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature inherent structure such as consistent spatial arrangement, shape or texture, all of which are critical for diagnosis. However, existing DPMs operate in noisy latent spaces that lack semantic structure and strong priors, making it difficult to ensure meaningful control over generated content. To address this, we propose graph-based object-level representations for Graph-Conditioned-Diffusion. Our approach generates graph nodes corresponding to each major structure in the image, encapsulating their individual features and relationships. These graph representations are processed by a transformer module and integrated into a diffusion model via the text-conditioning mechanism, enabling fine-grained control over generation. We evaluate this approach using a real-world histopathology use case, demonstrating that our generated data can reliably substitute for annotated patient data in downstream segmentation tasks. The code is available here.
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            <a href="https://www.alphaxiv.org/abs/2510.07126v1" target="_blank" rel="noopener noreferrer">
                脑肿瘤分割中多种归一化方法的验证：联邦学习能否克服这种异质性？
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            Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jan Fiszer, Dominika Ciupek, Maciej Malawski
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注医学领域的脑肿瘤分割和联邦学习技术，这属于明确的无关主题范畴。论文标题中提到的归一化方法验证和联邦学习都是与医疗图像处理相关的技术，与推荐系统、搜索或广告领域没有任何直接或间接的关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:21:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07126v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07126v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.DC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate data subsets, reflecting a common cause of heterogeneity. These subsets are then used for training and testing models for brain tumor segmentation. The findings provide insights into the influence of the MRI intensity normalization methods on segmentation models, both training and inference. Notably, the FL methods demonstrated resilience to inconsistently normalized data across clients, achieving the 3D Dice score of 92%, which is comparable to a centralized model (trained using all data). These results indicate that FL is a solution to effectively train high-performing models without violating data privacy, a crucial concern in medical applications. The code is available at: https://github.com/SanoScience/fl-varying-normalization.
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            <a href="https://www.alphaxiv.org/abs/2510.07119v1" target="_blank" rel="noopener noreferrer">
                MoRe：基于图优化的单目几何细化以实现跨视角一致性
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            MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongki Jung, Jaehoon Choi, Yonghan Lee, Sungmin Eum, Heesung Kwon, Dinesh Manoch...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的单目几何细化和跨视角一致性，属于纯粹的3D视觉研究。虽然涉及图优化技术，但论文内容与推荐系统、搜索或广告的核心技术领域没有直接关联，也没有展示在异构数据处理或Transformer架构方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 15:11:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07119v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07119v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment. To induce inter-frame relationships, our method employs feature matching between frames to establish correspondences. Rather than applying simple least squares optimization on these matched points, we formulate a graph-based optimization framework that performs local planar approximation using the estimated 3D points and surface normals estimated by monocular foundation models. This formulation addresses the scale ambiguity inherent in monocular geometric priors while preserving the underlying 3D structure. We further demonstrate that MoRe not only enhances 3D reconstruction but also improves novel view synthesis, particularly in sparse view rendering scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.07041v1" target="_blank" rel="noopener noreferrer">
                U-Bench：通过100种变体基准测试全面理解U-Net
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            U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fenghe Tang, Chengqi Dong, Wenxin Ma, Zikang Xu, Heqin Zhu, Zihang Jiang, Rongsh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的U-Net架构基准测试，属于纯粹的视觉研究范畴。虽然U-Net在图像分割中有广泛应用，但论文本身没有展示与推荐系统、搜索或广告的明确关联，也不涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 14:06:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07041v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07041v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread adoption, there is still no comprehensive benchmark to systematically evaluate their performance and utility, largely because of insufficient statistical validation and limited consideration of efficiency and generalization across diverse datasets. To bridge this gap, we present U-Bench, the first large-scale, statistically rigorous benchmark that evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates models along three key dimensions: statistical robustness, zero-shot generalization, and computational efficiency. We introduce a novel metric, U-Score, which jointly captures the performance-efficiency trade-off, offering a deployment-oriented perspective on model progress. (2) Systematic Analysis and Model Selection Guidance: We summarize key findings from the large-scale evaluation and systematically analyze the impact of dataset characteristics and architectural paradigms on model performance. Based on these insights, we propose a model advisor agent to guide researchers in selecting the most suitable models for specific datasets and tasks. (3) Public Availability: We provide all code, models, protocols, and weights, enabling the community to reproduce our results and extend the benchmark with future methods. In summary, U-Bench not only exposes gaps in previous evaluations but also establishes a foundation for fair, reproducible, and practically relevant benchmarking in the next decade of U-Net-based segmentation models. The project can be accessed at: https://fenghetan9.github.io/ubench. Code is available at: https://github.com/FengheTan9/U-Bench.
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            <a href="https://www.alphaxiv.org/abs/2510.07008v1" target="_blank" rel="noopener noreferrer">
                基于深度神经网络和隐马尔可夫模型的多年度作物类型分类贝叶斯建模
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            Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gianmarco Perantoni, Giulio Weikmann, Lorenzo Bruzzone
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于农业领域的作物分类问题，属于特定的领域应用，与推荐系统、搜索或广告的核心技术无关。论文虽然涉及深度神经网络，但应用场景和问题定义与我的关注领域没有直接关联，属于明确的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:33:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.07008v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.07008v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs) integrated with Transformer Encoder (TE) based DNNs. The proposed approach aims to capture both i) intricate temporal correlations in yearly SITS and ii) specific patterns in multiyear crop type sequences. It leverages the cascade classification of an HMM layer built on top of the TE, discerning consistent yearly crop-type sequences. Validation on a multiyear crop type classification dataset spanning 47 crop types and six years of Sentinel-2 acquisitions demonstrates the importance of modelling temporal consistency in the predicted labels. HMMs enhance the overall performance and F1 scores, emphasising the effectiveness of the proposed approach.
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            <a href="https://www.alphaxiv.org/abs/2510.06988v1" target="_blank" rel="noopener noreferrer">
                无需动作捕捉：仅使用文本提示通过强化学习对后训练运动扩散模型进行优化
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            No MoCap Needed: Post-Training Motion Diffusion Models with Reinforcement Learning using Only Textual Prompts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Girolamo Macaluso, Lorenzo Mandelli, Mirko Bicchierai, Stefano Berretti, Andrew ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于运动生成和扩散模型，属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了强化学习，但应用场景仅限于运动生成，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 13:12:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06988v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06988v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Diffusion models have recently advanced human motion generation, producing realistic and diverse animations from textual prompts. However, adapting these models to unseen actions or styles typically requires additional motion capture data and full retraining, which is costly and difficult to scale. We propose a post-training framework based on Reinforcement Learning that fine-tunes pretrained motion diffusion models using only textual prompts, without requiring any motion ground truth. Our approach employs a pretrained text-motion retrieval network as a reward signal and optimizes the diffusion policy with Denoising Diffusion Policy Optimization, effectively shifting the model's generative distribution toward the target domain without relying on paired motion data. We evaluate our method on cross-dataset adaptation and leave-one-out motion experiments using the HumanML3D and KIT-ML datasets across both latent- and joint-space diffusion architectures. Results from quantitative metrics and user studies show that our approach consistently improves the quality and diversity of generated motions, while preserving performance on the original distribution. Our approach is a flexible, data-efficient, and privacy-preserving solution for motion adaptation.
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            <a href="https://www.alphaxiv.org/abs/2510.06973v1" target="_blank" rel="noopener noreferrer">
                解决长视频字幕生成中的ID匹配挑战
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            Addressing the ID-Matching Challenge in Long Video Captioning
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhantao Yang, Huangji Wang, Ruili Feng, Han Zhang, Yuting Hu, Shangwen Zhu, Juny...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频字幕生成中的ID匹配问题，这属于纯粹的视觉-语言任务，与推荐系统、搜索或广告的核心技术没有直接关联。虽然标题提到了'长视频'，但这仍然属于计算机视觉和自然语言处理的交叉领域，没有明确的推荐、搜索或广告应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:59:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06973v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06973v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating captions for long and complex videos is both critical and challenging, with significant implications for the growing fields of text-to-video generation and multi-modal understanding. One key challenge in long video captioning is accurately recognizing the same individuals who appear in different frames, which we refer to as the ID-Matching problem. Few prior works have focused on this important issue. Those that have, usually suffer from limited generalization and depend on point-wise matching, which limits their overall effectiveness. In this paper, unlike previous approaches, we build upon LVLMs to leverage their powerful priors. We aim to unlock the inherent ID-Matching capabilities within LVLMs themselves to enhance the ID-Matching performance of captions. Specifically, we first introduce a new benchmark for assessing the ID-Matching capabilities of video captions. Using this benchmark, we investigate LVLMs containing GPT-4o, revealing key insights that the performance of ID-Matching can be improved through two methods: 1) enhancing the usage of image information and 2) increasing the quantity of information of individual descriptions. Based on these insights, we propose a novel video captioning method called Recognizing Identities for Captioning Effectively (RICE). Extensive experiments including assessments of caption quality and ID-Matching performance, demonstrate the superiority of our approach. Notably, when implemented on GPT-4o, our RICE improves the precision of ID-Matching from 50% to 90% and improves the recall of ID-Matching from 15% to 80% compared to baseline. RICE makes it possible to continuously track different individuals in the captions of long videos.
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                基于查询学习全局表征用于矢量化高精地图构建
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            Learning Global Representation from Queries for Vectorized HD Map Construction
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shoumeng Qiu, Xinrun Li, Yang Long, Xiangyang Xue, Varun Ojha, Jian Pu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于高精地图构建的计算机视觉任务，属于自动驾驶领域。虽然涉及查询学习和表征学习技术，但其应用场景（HD地图构建）与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及Transformer架构或LLM技术的潜在应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:56:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06969v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06969v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The online construction of vectorized high-definition (HD) maps is a cornerstone of modern autonomous driving systems. State-of-the-art approaches, particularly those based on the DETR framework, formulate this as an instance detection problem. However, their reliance on independent, learnable object queries results in a predominantly local query perspective, neglecting the inherent global representation within HD maps. In this work, we propose \textbf{MapGR} (\textbf{G}lobal \textbf{R}epresentation learning for HD \textbf{Map} construction), an architecture designed to learn and utilize a global representations from queries. Our method introduces two synergistic modules: a Global Representation Learning (GRL) module, which encourages the distribution of all queries to better align with the global map through a carefully designed holistic segmentation task, and a Global Representation Guidance (GRG) module, which endows each individual query with explicit, global-level contextual information to facilitate its optimization. Evaluations on the nuScenes and Argoverse2 datasets validate the efficacy of our approach, demonstrating substantial improvements in mean Average Precision (mAP) compared to leading baselines.
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            <a href="https://www.alphaxiv.org/abs/2510.06967v1" target="_blank" rel="noopener noreferrer">
                使用2D高斯泼溅生成文本到3D的表面
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Generating Surface for Text-to-3D using 2D Gaussian Splatting
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huanning Dong, Fan Li, Ping Kuang, Jianwen Min
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于文本到3D生成和计算机图形学技术，属于纯粹的3D视觉领域。虽然标题提到文本输入，但这与推荐系统、搜索或广告的核心技术没有直接关联，也不涉及Transformer架构改进或LLM在推荐/搜索中的应用。该技术主要服务于3D内容生成，属于被排除的无关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:54:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06967v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06967v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent advancements in Text-to-3D modeling have shown significant potential for the creation of 3D content. However, due to the complex geometric shapes of objects in the natural world, generating 3D content remains a challenging task. Current methods either leverage 2D diffusion priors to recover 3D geometry, or train the model directly based on specific 3D representations. In this paper, we propose a novel method named DirectGaussian, which focuses on generating the surfaces of 3D objects represented by surfels. In DirectGaussian, we utilize conditional text generation models and the surface of a 3D object is rendered by 2D Gaussian splatting with multi-view normal and texture priors. For multi-view geometric consistency problems, DirectGaussian incorporates curvature constraints on the generated surface during optimization process. Through extensive experiments, we demonstrate that our framework is capable of achieving diverse and high-fidelity 3D content creation.
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            <a href="https://www.alphaxiv.org/abs/2510.06952v1" target="_blank" rel="noopener noreferrer">
                OBJVanish：物理可实现的文本到3D对抗生成激光雷达不可见物体
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            OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bing Li, Wuqi Wang, Yanan Zhang, Jingzheng Li, Haigen Min, Wei Feng, Xingyu Zhao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D物体生成和激光雷达感知对抗攻击，属于计算机视觉和3D生成领域。虽然标题提到文本到3D生成，但核心关注点是物理可实现的对抗攻击，与推荐系统、搜索或广告的技术焦点没有直接关联。该研究主要面向自动驾驶安全领域，不具备在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 12:35:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06952v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06952v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is essential for thoroughly testing these detection systems and exposing their vulnerabilities before real-world deployment. However, existing adversarial attacks that add optimized perturbations to 3D points have two critical limitations: they rarely cause complete object disappearance and prove difficult to implement in physical environments. We introduce the text-to-3D adversarial generation method, a novel approach enabling physically realizable attacks that can generate 3D models of objects truly invisible to LiDAR detectors and be easily realized in the real world. Specifically, we present the first empirical study that systematically investigates the factors influencing detection vulnerability by manipulating the topology, connectivity, and intensity of individual pedestrian 3D models and combining pedestrians with multiple objects within the CARLA simulation environment. Building on the insights, we propose the physically-informed text-to-3D adversarial generation (Phy3DAdvGen) that systematically optimizes text prompts by iteratively refining verbs, objects, and poses to produce LiDAR-invisible pedestrians. To ensure physical realizability, we construct a comprehensive object pool containing 13 3D models of real objects and constrain Phy3DAdvGen to generate 3D objects based on combinations of objects in this set. Extensive experiments demonstrate that our approach can generate 3D pedestrians that evade six state-of-the-art (SOTA) LiDAR 3D detectors in both CARLA simulation and physical environments, thereby highlighting vulnerabilities in safety-critical applications.
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            <a href="https://www.alphaxiv.org/abs/2510.06887v1" target="_blank" rel="noopener noreferrer">
                基于条件TransMix增强与交叉注意力Transformer的肺部感染严重程度预测
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            Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bouthaina Slika, Fadi Dornaika, Fares Bougourzi, Karim Hammoudi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的肺部感染严重程度预测，属于明确的医疗应用范畴。虽然使用了Transformer架构，但该应用与推荐系统、搜索或广告领域没有任何关联，完全属于被排除的医疗领域特定应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 11:08:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06887v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06887v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity. Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel encoders, a cross-gated attention mechanism, and a feature aggregator to capture rich multi-scale features; and (ii) Conditional Online TransMix, a custom data augmentation strategy designed to address dataset imbalance by generating mixed-label image patches during training. Evaluated on two benchmark datasets, RALO CXR and Per-COVID-19 CT, our method consistently outperforms several state-of-the-art deep learning models. The results emphasize the critical role of data augmentation and gated attention in improving both robustness and predictive accuracy. This approach offers a reliable, adaptable tool to support clinical diagnosis, disease monitoring, and personalized treatment planning. The source code of this work is available at https://github.com/bouthainas/QCross-Att-PVT.
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            <a href="https://www.alphaxiv.org/abs/2510.06876v1" target="_blank" rel="noopener noreferrer">
                HARP-NeXt：用于3D LiDAR语义分割的高速精确范围-点融合网络
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            HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于3D LiDAR语义分割，属于计算机视觉和3D感知领域，与推荐系统、搜索或广告没有直接关联。该技术主要应用于自动驾驶、机器人导航等场景，无法看出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:46:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06876v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06876v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D projection. Additionally, many recent methods rely on test-time augmentation (TTA) to improve performance, which further slows the inference. Moreover, the pre-processing phase across all methods increases execution time and is demanding on embedded platforms. Therefore, we introduce HARP-NeXt, a high-speed and accurate LiDAR semantic segmentation network. We first propose a novel pre-processing methodology that significantly reduces computational overhead. Then, we design the Conv-SE-NeXt feature extraction block to efficiently capture representations without deep layer stacking per network stage. We also employ a multi-scale range-point fusion backbone that leverages information at multiple abstraction levels to preserve essential geometric details, thereby enhancing accuracy. Experiments on the nuScenes and SemanticKITTI benchmarks show that HARP-NeXt achieves a superior speed-accuracy trade-off compared to all state-of-the-art methods, and, without relying on ensemble models or TTA, is comparable to the top-ranked PTv3, while running 24$\times$ faster. The code is available at https://github.com/SamirAbouHaidar/HARP-NeXt
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            <a href="https://www.alphaxiv.org/abs/2510.06858v1" target="_blank" rel="noopener noreferrer">
                解释原始数据复杂性以改进卫星星上处理
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            Explaining raw data complexity to improve satellite onboard processing
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adrien Dorise, Marjorie Bellizzi, Adrien Girard, Benjamin Francesconi, Stéphane ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卫星数据处理和航天应用，与推荐系统、搜索或广告领域没有任何关联。论文标题表明其核心是卫星星上处理和原始数据复杂性分析，这属于航天工程和遥感技术领域，完全超出了我的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 10:26:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06858v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06858v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11s and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.
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            <a href="https://www.alphaxiv.org/abs/2510.06829v1" target="_blank" rel="noopener noreferrer">
                仅使用事件相机的网格分配实时线段特征检测与跟踪
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mikihiro Ikura, Arren Glover, Masayoshi Mizuno, Chiara Bartolozzi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的事件相机技术和线段特征检测，属于纯粹的视觉处理领域。虽然事件相机技术可能有潜在的感知应用，但论文标题没有显示出与推荐系统、搜索或广告的任何直接联系，也没有涉及LLM、Transformer架构或异构数据建模等核心关注领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:52:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06829v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06829v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Line segment extraction is effective for capturing geometric features of human-made environments. Event-based cameras, which asynchronously respond to contrast changes along edges, enable efficient extraction by reducing redundant data. However, recent methods often rely on additional frame cameras or struggle with high event rates. This research addresses real-time line segment detection and tracking using only a modern, high-resolution (i.e., high event rate) event-based camera. Our lattice-allocated pipeline consists of (i) velocity-invariant event representation, (ii) line segment detection based on a fitting score, (iii) and line segment tracking by perturbating endpoints. Evaluation using ad-hoc recorded dataset and public datasets demonstrates real-time performance and higher accuracy compared to state-of-the-art event-only and event-frame hybrid baselines, enabling fully stand-alone event camera operation in real-world settings.
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            <a href="https://www.alphaxiv.org/abs/2510.06809v1" target="_blank" rel="noopener noreferrer">
                VA-Adapter：将超声基础模型适配至超声心动图探头引导
            </a>
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            VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Teng Wang, Haojun Jiang, Yuxuan Wang, Zhenguo Sun, Shiji Song, Gao Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学超声成像领域的特定应用——超声心动图探头引导，属于明确的医学领域应用。其技术内容（超声基础模型适配）与推荐系统、搜索、广告等核心关注领域完全无关，也不涉及任何可能应用于这些领域的LLM或Transformer技术进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:38:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06809v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06809v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Echocardiography is a critical tool for detecting heart diseases. Recently, ultrasound foundation models have demonstrated remarkable capabilities in cardiac ultrasound image analysis. However, obtaining high-quality ultrasound images is a prerequisite for accurate diagnosis. Due to the exceptionally high operational difficulty of cardiac ultrasound, there is a shortage of highly skilled personnel, which hinders patients from receiving timely examination services. In this paper, we aim to adapt the medical knowledge learned by foundation models from vast datasets to the probe guidance task, which is designed to provide real-time operational recommendations for junior sonographers to acquire high-quality ultrasound images. Moreover, inspired by the practice where experts optimize action decisions based on past explorations, we meticulously design a parameter-efficient Vision-Action Adapter (VA-Adapter) to enable foundation model's image encoder to encode vision-action sequences, thereby enhancing guidance performance. With built-in sequential reasoning capabilities in a compact design, the VA-Adapter enables a pre-trained ultrasound foundation model to learn precise probe adjustment strategies by fine-tuning only a small subset of parameters. Extensive experiments demonstrate that the VA-Adapter can surpass strong probe guidance models. Our code will be released after acceptance.
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            <a href="https://www.alphaxiv.org/abs/2510.06802v1" target="_blank" rel="noopener noreferrer">
                捕获与交互：基于高斯泼溅在Unity中实现快速3D物体采集与渲染
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            Capture and Interact: Rapid 3D Object Acquisition and Rendering with Gaussian Splatting in Unity
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Islomjon Shukhratov, Sergey Gorinsky
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D计算机视觉中的物体采集和渲染技术，使用高斯泼溅方法在Unity引擎中实现。虽然技术本身具有创新性，但主要应用于3D图形和虚拟现实领域，与推荐系统、搜索或广告的核心技术栈没有直接关联，也没有明显的潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:31:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06802v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06802v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                    Capturing and rendering three-dimensional (3D) objects in real time remain a significant challenge, yet hold substantial potential for applications in augmented reality, digital twin systems, remote collaboration and prototyping. We present an end-to-end pipeline that leverages 3D Gaussian Splatting (3D GS) to enable rapid acquisition and interactive rendering of real-world objects using a mobile device, cloud processing and a local computer. Users scan an object with a smartphone video, upload it for automated 3D reconstruction, and visualize it interactively in Unity at an average of 150 frames per second (fps) on a laptop. The system integrates mobile capture, cloud-based 3D GS and Unity rendering to support real-time telepresence. Our experiments show that the pipeline processes scans in approximately 10 minutes on a graphics processing unit (GPU) achieving real-time rendering on the laptop.
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            <a href="https://www.alphaxiv.org/abs/2510.06791v1" target="_blank" rel="noopener noreferrer">
                极端非模态人脸检测
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        <div class="mb-2 text-base text-gray-700">
            Extreme Amodal Face Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Changlin Song, Yunzhong Hou, Michael Randall Barnes, Rahul Shome, Dylan Campbell
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的人脸检测任务，特别是处理非模态（部分遮挡）情况。虽然人脸检测在广告中有潜在应用（如定向广告），但这属于纯粹的视觉技术，没有明确的推荐系统、搜索或广告排名相关性，也不涉及LLM、Transformer架构或多模态建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:22:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06791v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06791v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Extreme amodal detection is the task of inferring the 2D location of objects that are not fully visible in the input image but are visible within an expanded field-of-view. This differs from amodal detection, where the object is partially visible within the input image, but is occluded. In this paper, we consider the sub-problem of face detection, since this class provides motivating applications involving safety and privacy, but do not tailor our method specifically to this class. Existing approaches rely on image sequences so that missing detections may be interpolated from surrounding frames or make use of generative models to sample possible completions. In contrast, we consider the single-image task and propose a more efficient, sample-free approach that makes use of the contextual cues from the image to infer the presence of unseen faces. We design a heatmap-based extreme amodal object detector that addresses the problem of efficiently predicting a lot (the out-of-frame region) from a little (the image) with a selective coarse-to-fine decoder. Our method establishes strong results for this new task, even outperforming less efficient generative approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.06784v1" target="_blank" rel="noopener noreferrer">
                Bionetta：高效的客户端零知识机器学习证明
            </a>
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            Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dmytro Zakharov, Oleksandr Kurbatov, Artem Sdobnov, Lev Soukhanov, Yevhenii Sekh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及零知识证明和机器学习，属于隐私和安全技术范畴，这在无关主题中被明确排除。虽然机器学习证明在理论上可能应用于推荐系统，但论文标题强调的是客户端零知识证明，这主要关注隐私保护，而非推荐、搜索或广告领域的核心进展或使能技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 09:10:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06784v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06784v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.
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            <a href="https://www.alphaxiv.org/abs/2510.06769v1" target="_blank" rel="noopener noreferrer">
                基于粗粒度标签的高分辨率土地覆盖制图深度多示例学习方法
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            A deep multiple instance learning approach based on coarse labels for high-resolution land-cover mapping
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gianmarco Perantoni, Lorenzo Bruzzone
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像分析和土地覆盖制图，属于地理信息科学领域，与推荐系统、搜索或广告的核心技术完全无关。论文中的深度多示例学习方法虽然涉及机器学习，但其应用场景和问题定义与我的关注领域没有任何交集，且没有显示出在推荐系统、搜索或广告中的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:50:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06769v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06769v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The quantity and the quality of the training labels are central problems in high-resolution land-cover mapping with machine-learning-based solutions. In this context, weak labels can be gathered in large quantities by leveraging on existing low-resolution or obsolete products. In this paper, we address the problem of training land-cover classifiers using high-resolution imagery (e.g., Sentinel-2) and weak low-resolution reference data (e.g., MODIS -derived land-cover maps). Inspired by recent works in Deep Multiple Instance Learning (DMIL), we propose a method that trains pixel-level multi-class classifiers and predicts low-resolution labels (i.e., patch-level classification), where the actual high-resolution labels are learned implicitly without direct supervision. This is achieved with flexible pooling layers that are able to link the semantics of the pixels in the high-resolution imagery to the low-resolution reference labels. Then, the Multiple Instance Learning (MIL) problem is re-framed in a multi-class and in a multi-label setting. In the former, the low-resolution annotation represents the majority of the pixels in the patch. In the latter, the annotation only provides us information on the presence of one of the land-cover classes in the patch and thus multiple labels can be considered valid for a patch at a time, whereas the low-resolution labels provide us only one label. Therefore, the classifier is trained with a Positive-Unlabeled Learning (PUL) strategy. Experimental results on the 2020 IEEE GRSS Data Fusion Contest dataset show the effectiveness of the proposed framework compared to standard training strategies.
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                OBS-Diff：扩散模型的一次性精确剪枝
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        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junhan Zhu, Hesong Wang, Mingluo Su, Zefang Wang, Huan Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于扩散模型的模型压缩技术，属于纯粹的生成式AI领域优化。虽然剪枝技术本身具有通用性，但论文标题明确限定于扩散模型，而扩散模型在推荐系统、搜索或广告中的直接应用非常有限，主要适用于内容生成场景，这属于被排除的AIGC范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 08:19:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06751v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06751v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon (OBS), adapting it to the complex architectures of modern diffusion models and supporting diverse pruning granularity, including unstructured, N:M semi-structured, and structured (MHA heads and FFN neurons) sparsity; (ii) To align the pruning criteria with the iterative dynamics of the diffusion process, by examining the problem from an error-accumulation perspective, we propose a novel timestep-aware Hessian construction that incorporates a logarithmic-decrease weighting scheme, assigning greater importance to earlier timesteps to mitigate potential error accumulation; (iii) Furthermore, a computationally efficient group-wise sequential pruning strategy is proposed to amortize the expensive calibration process. Extensive experiments show that OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering inference acceleration with minimal degradation in visual quality.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06694v1" target="_blank" rel="noopener noreferrer">
                SCas4D：用于提升持久性4D新视角合成的结构级联优化
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            SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于4D新视角合成，属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及优化技术，但其应用场景（4D视觉合成）与我的关注领域相距甚远，且没有明显的技术迁移潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:39:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06694v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06694v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world deformations often exhibit hierarchical patterns, where groups of Gaussians share similar transformations. By progressively refining deformations from coarse part-level to fine point-level, SCas4D achieves convergence within 100 iterations per time frame and produces results comparable to existing methods with only one-twentieth of the training iterations. The approach also demonstrates effectiveness in self-supervised articulated object segmentation, novel view synthesis, and dense point tracking tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.06687v1" target="_blank" rel="noopener noreferrer">
                基于光场与激光雷达融合的语义分割算法
            </a>
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            Semantic Segmentation Algorithm Based on Light Field and LiDAR Fusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jie Luo, Yuxuan Jiang, Xin Jin, Mingyu Liu, Yihui Fan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的语义分割技术，结合光场和激光雷达两种视觉传感器。虽然涉及多模态融合，但属于纯粹的视觉感知研究，与推荐系统、搜索或广告的核心技术没有直接关联，也没有明显的Transformer架构或LLM应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 06:15:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06687v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under complex conditions such as occlusion. Light field and LiDAR modalities provide complementary visual and spatial cues that are beneficial for robust perception; however, their effective integration is hindered by limited viewpoint diversity and inherent modality discrepancies. To address these challenges, the first multimodal semantic segmentation dataset integrating light field data and point cloud data is proposed. Based on this dataset, we proposed a multi-modal light field point-cloud fusion segmentation network(Mlpfseg), incorporating feature completion and depth perception to segment both camera images and LiDAR point clouds simultaneously. The feature completion module addresses the density mismatch between point clouds and image pixels by performing differential reconstruction of point-cloud feature maps, enhancing the fusion of these modalities. The depth perception module improves the segmentation of occluded objects by reinforcing attention scores for better occlusion awareness. Our method outperforms image-only segmentation by 1.71 Mean Intersection over Union(mIoU) and point cloud-only segmentation by 2.38 mIoU, demonstrating its effectiveness.
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            <a href="https://www.alphaxiv.org/abs/2510.06669v1" target="_blank" rel="noopener noreferrer">
                面向工业缺陷检测的自动化神经网络架构设计
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        <div class="mb-2 text-base text-gray-700">
            Automated Neural Architecture Design for Industrial Defect Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxi Liu, Yunfeng Ma, Yi Tang, Min Liu, Shuai Jiang, Yaonan Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于工业缺陷检测这一计算机视觉应用领域，属于纯粹的视觉任务，与推荐系统、搜索或广告没有直接关联。虽然提到了神经网络架构设计，但属于特定领域应用而非通用的Transformer架构或LLM技术进步，无法应用于RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 05:37:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06669v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06669v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability. Due to the diverse shapes and sizes of surface defects, SDD faces two main challenges: intraclass difference and interclass similarity. Existing methods primarily utilize manually designed models, which require extensive trial and error and often struggle to address both challenges effectively. To overcome this, we propose AutoNAD, an automated neural architecture design framework for SDD that jointly searches over convolutions, transformers, and multi-layer perceptrons. This hybrid design enables the model to capture both fine-grained local variations and long-range semantic context, addressing the two key challenges while reducing the cost of manual network design. To support efficient training of such a diverse search space, AutoNAD introduces a cross weight sharing strategy, which accelerates supernet convergence and improves subnet performance. Additionally, a searchable multi-level feature aggregation module (MFAM) is integrated to enhance multi-scale feature learning. Beyond detection accuracy, runtime efficiency is essential for industrial deployment. To this end, AutoNAD incorporates a latency-aware prior to guide the selection of efficient architectures. The effectiveness of AutoNAD is validated on three industrial defect datasets and further applied within a defect imaging and detection platform. Code will be available at https://github.com/Yuxi104/AutoNAD.
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            <a href="https://www.alphaxiv.org/abs/2510.06629v1" target="_blank" rel="noopener noreferrer">
                脉冲神经网络的非监督后门检测与缓解
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        <div class="mb-2 text-base text-gray-700">
            Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiachen Li, Bang Wu, Xiaoyu Xia, Xiaoning Liu, Xun Yi, Xiuzhen Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脉冲神经网络的后门安全问题，属于安全领域范畴。这与我的关注点完全无关，我的关注领域明确排除了安全、隐私等非技术性话题，专注于推荐系统、搜索、广告及相关使能技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:25:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06629v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06629v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Spiking Neural Networks (SNNs) have gained increasing attention for their superior energy efficiency compared to Artificial Neural Networks (ANNs). However, their security aspects, particularly under backdoor attacks, have received limited attention. Existing defense methods developed for ANNs perform poorly or can be easily bypassed in SNNs due to their event-driven and temporal dependencies. This paper identifies the key blockers that hinder traditional backdoor defenses in SNNs and proposes an unsupervised post-training detection framework, Temporal Membrane Potential Backdoor Detection (TMPBD), to overcome these challenges. TMPBD leverages the maximum margin statistics of temporal membrane potential (TMP) in the final spiking layer to detect target labels without any attack knowledge or data access. We further introduce a robust mitigation mechanism, Neural Dendrites Suppression Backdoor Mitigation (NDSBM), which clamps dendritic connections between early convolutional layers to suppress malicious neurons while preserving benign behaviors, guided by TMP extracted from a small, clean, unlabeled dataset. Extensive experiments on multiple neuromorphic benchmarks and state-of-the-art input-aware dynamic trigger attacks demonstrate that TMPBD achieves 100% detection accuracy, while NDSBM reduces the attack success rate from 100% to 8.44%, and to 2.81% when combined with detection, without degrading clean accuracy.
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            <a href="https://www.alphaxiv.org/abs/2510.06621v1" target="_blank" rel="noopener noreferrer">
                FEAorta：基于3D CT图像的主动脉有限元分析全自动框架
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            FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiasong Chen, Linchen Qian, Ruonan Gong, Christina Sun, Tongran Qin, Thuy Pham, ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1"></p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 04:00:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06621v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06621v1
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                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CE</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.
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            <a href="https://www.alphaxiv.org/abs/2510.06619v1" target="_blank" rel="noopener noreferrer">
                MSITrack：一个用于多光谱单目标跟踪的挑战性基准
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            MSITrack: A Challenging Benchmark for Multispectral Single Object Tracking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Feng, Tingfa Xu, Haolin Qin, Tianhao Li, Shuaihao Han, Xuyang Zou, Zhan Lv, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的多光谱目标跟踪基准，属于纯粹的视觉研究方向。虽然多模态学习在概念上与异构数据处理相关，但该论文没有展示与推荐系统、搜索或广告领域的明确联系，也没有涉及Transformer架构或LLM技术的应用潜力。</p>
        </div>
        
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:56:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06619v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06619v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Visual object tracking in real-world scenarios presents numerous challenges including occlusion, interference from similar objects and complex backgrounds-all of which limit the effectiveness of RGB-based trackers. Multispectral imagery, which captures pixel-level spectral reflectance, enhances target discriminability. However, the availability of multispectral tracking datasets remains limited. To bridge this gap, we introduce MSITrack, the largest and most diverse multispectral single object tracking dataset to date. MSITrack offers the following key features: (i) More Challenging Attributes-including interference from similar objects and similarity in color and texture between targets and backgrounds in natural scenarios, along with a wide range of real-world tracking challenges; (ii) Richer and More Natural Scenes-spanning 55 object categories and 300 distinct natural scenes, MSITrack far exceeds the scope of existing benchmarks. Many of these scenes and categories are introduced to the multispectral tracking domain for the first time; (iii) Larger Scale-300 videos comprising over 129k frames of multispectral imagery. To ensure annotation precision, each frame has undergone meticulous processing, manual labeling and multi-stage verification. Extensive evaluations using representative trackers demonstrate that the multispectral data in MSITrack significantly improves performance over RGB-only baselines, highlighting its potential to drive future advancements in the field. The MSITrack dataset is publicly available at: https://github.com/Fengtao191/MSITrack.
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            <a href="https://www.alphaxiv.org/abs/2510.06612v1" target="_blank" rel="noopener noreferrer">
                从音频到视频的桥梁：音素-视位对齐使每张脸能够说多种语言
            </a>
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            A Bridge from Audio to Video: Phoneme-Viseme Alignment Allows Every Face to Speak Multiple Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zibo Su, Kun Wei, Jiahua Li, Xu Yang, Cheng Deng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频-视频模态对齐的跨模态技术，属于计算机视觉和语音处理的交叉领域。虽然涉及多模态建模，但其核心应用场景是语音驱动的人脸动画和语言翻译，与推荐系统、搜索或广告的排名和建模需求没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:46:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06612v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06612v1
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                    Speech-driven talking face synthesis (TFS) focuses on generating lifelike facial animations from audio input. Current TFS models perform well in English but unsatisfactorily in non-English languages, producing wrong mouth shapes and rigid facial expressions. The terrible performance is caused by the English-dominated training datasets and the lack of cross-language generalization abilities. Thus, we propose Multilingual Experts (MuEx), a novel framework featuring a Phoneme-Guided Mixture-of-Experts (PG-MoE) architecture that employs phonemes and visemes as universal intermediaries to bridge audio and video modalities, achieving lifelike multilingual TFS. To alleviate the influence of linguistic differences and dataset bias, we extract audio and video features as phonemes and visemes respectively, which are the basic units of speech sounds and mouth movements. To address audiovisual synchronization issues, we introduce the Phoneme-Viseme Alignment Mechanism (PV-Align), which establishes robust cross-modal correspondences between phonemes and visemes. In addition, we build a Multilingual Talking Face Benchmark (MTFB) comprising 12 diverse languages with 95.04 hours of high-quality videos for training and evaluating multilingual TFS performance. Extensive experiments demonstrate that MuEx achieves superior performance across all languages in MTFB and exhibits effective zero-shot generalization to unseen languages without additional training.
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            <a href="https://www.alphaxiv.org/abs/2510.06611v1" target="_blank" rel="noopener noreferrer">
                基于隐式表示正则化的自监督物理引导模型用于快速MRI重建
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            Self-supervised Physics-guided Model with Implicit Representation Regularization for Fast MRI Reconstruction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingran Xu, Yuanyuan Liu, Yanjie Zhu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像(MRI)重建这一特定领域应用，与推荐系统、搜索或广告的核心技术领域完全无关。虽然提到了自监督学习和表示正则化等技术概念，但这些技术在医学影像处理中的应用与RecSys/Search/Ads领域没有直接关联或潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:40:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06611v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06611v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Magnetic Resonance Imaging (MRI) is a vital clinical diagnostic tool, yet its widespread application is limited by prolonged scan times. Fast MRI reconstruction techniques effectively reduce acquisition duration by reconstructing high-fidelity MR images from undersampled k-space data. In recent years, deep learning-based methods have demonstrated remarkable progress in this field, with self-supervised and unsupervised learning approaches proving particularly valuable in scenarios where fully sampled data are difficult to obtain. This paper proposes a novel zero-shot self-supervised reconstruction framework named UnrollINR, which enables scan-specific MRI reconstruction without relying on external training data. The method adopts a physics-guided unrolled iterative reconstruction architecture and introduces Implicit Neural Representation (INR) as a regularization prior to effectively constrain the solution space. By combining a deep unrolled structure with the powerful implicit representation capability of INR, the model's interpretability and reconstruction performance are enhanced. Experimental results demonstrate that even at a high acceleration rate of 10, UnrollINR achieves superior reconstruction performance compared to the supervised learning method, validating the superiority of the proposed method.
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            <a href="https://www.alphaxiv.org/abs/2510.06601v1" target="_blank" rel="noopener noreferrer">
                AIM 2025真实世界RAW图像去噪挑战赛
            </a>
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            AIM 2025 Challenge on Real-World RAW Image Denoising
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feiran Li, Jiacheng Li, Marcos V. Conde, Beril Besbinar, Vlad Hosu, Daisuke Iso,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像去噪技术，特别是针对RAW格式图像的处理。虽然图像质量可能间接影响推荐系统中的视觉内容，但该工作本身是纯粹的视觉处理任务，没有涉及推荐、搜索或广告系统的核心算法，也不包含LLM或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:22:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06601v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06601v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is built upon a newly established evaluation benchmark featuring challenging low-light noisy images captured in the wild using five different DSLR cameras. Participants are tasked with developing novel noise synthesis pipelines, network architectures, and training methodologies to achieve high performance across different camera models. Winners are determined based on a combination of performance metrics, including full-reference measures (PSNR, SSIM, LPIPS), and non-reference ones (ARNIQA, TOPIQ). By pushing the boundaries of camera-agnostic low-light RAW image denoising trained on synthetic data, the competition promotes the development of robust and practical models aligned with the rapid progress in digital photography. We expect the competition outcomes to influence multiple domains, from image restoration to night-time autonomous driving.
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            <a href="https://www.alphaxiv.org/abs/2510.06596v1" target="_blank" rel="noopener noreferrer">
                SDQM：用于目标检测数据集评估的合成数据质量指标
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            SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的目标检测数据集评估，与推荐系统、搜索或广告的核心技术领域没有直接关联。虽然合成数据生成在理论上可能用于数据增强，但该论文主要关注视觉数据质量评估，缺乏明确的RecSys/Search/Ads应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 03:01:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06596v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06596v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IT</span><span class="category-tag">cs.LG</span><span class="category-tag">math.IT</span></div>
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                    The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through simulations and generative models has emerged as a promising solution, enhancing dataset diversity and improving the performance, reliability, and resilience of models. However, evaluating the quality of this generated data requires an effective metric. This paper introduces the Synthetic Dataset Quality Metric (SDQM) to assess data quality for object detection tasks without requiring model training to converge. This metric enables more efficient generation and selection of synthetic datasets, addressing a key challenge in resource-constrained object detection tasks. In our experiments, SDQM demonstrated a strong correlation with the mean Average Precision (mAP) scores of YOLOv11, a leading object detection model, while previous metrics only exhibited moderate or weak correlations. Additionally, it provides actionable insights for improving dataset quality, minimizing the need for costly iterative training. This scalable and efficient metric sets a new standard for evaluating synthetic data. The code for SDQM is available at https://github.com/ayushzenith/SDQM
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            <a href="https://www.alphaxiv.org/abs/2510.06592v1" target="_blank" rel="noopener noreferrer">
                面向跨域医学组织学的自适应染色归一化
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            Adaptive Stain Normalization for Cross-Domain Medical Histology
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito, Benjamin D. Haeff...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像处理中的染色归一化技术，属于医学领域的特定应用。虽然涉及跨域适应概念，但其应用场景（医学组织学）和核心方法（染色归一化）与推荐系统、搜索或广告领域完全无关，也不涉及LLM或Transformer架构的任何相关技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:53:28
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                <a href="https://arxiv.org/abs/2510.06592v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06592v1
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                    Deep learning advances have revolutionized automated digital pathology analysis. However, differences in staining protocols and imaging conditions can introduce significant color variability. In deep learning, such color inconsistency often reduces performance when deploying models on data acquired under different conditions from the training data, a challenge known as domain shift. Many existing methods attempt to address this problem via color normalization but suffer from several notable drawbacks such as introducing artifacts or requiring careful choice of a template image for stain mapping. To address these limitations, we propose a trainable color normalization model that can be integrated with any backbone network for downstream tasks such as object detection and classification. Based on the physics of the imaging process per the Beer-Lambert law, our model architecture is derived via algorithmic unrolling of a nonnegative matrix factorization (NMF) model to extract stain-invariant structural information from the original pathology images, which serves as input for further processing. Experimentally, we evaluate the method on publicly available pathology datasets and an internally curated collection of malaria blood smears for cross-domain object detection and classification, where our method outperforms many state-of-the-art stain normalization methods. Our code is available at https://github.com/xutianyue/BeerLaNet.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06584v1" target="_blank" rel="noopener noreferrer">
                通过领域自适应改进CT深度学习模型的伪影鲁棒性，无需带标签的伪影图像
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Improving Artifact Robustness for CT Deep Learning Models Without Labeled Artifact Images via Domain Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Justin Cheung, Samuel Savine, Calvin Nguyen, Lin Lu, Alhassan S. Yasin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（CT扫描）中的深度学习模型鲁棒性改进，属于医疗领域的特定应用。虽然涉及领域自适应技术，但其应用场景（医学影像伪影处理）与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等核心技术。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:27:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06584v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06584v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">q-bio.TO</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deep learning models which perform well on images from their training distribution can degrade substantially when applied to new distributions. If a CT scanner introduces a new artifact not present in the training labels, the model may misclassify the images. Although modern CT scanners include design features which mitigate these artifacts, unanticipated or difficult-to-mitigate artifacts can still appear in practice. The direct solution of labeling images from this new distribution can be costly. As a more accessible alternative, this study evaluates domain adaptation as an approach for training models that maintain classification performance despite new artifacts, even without corresponding labels. We simulate ring artifacts from detector gain error in sinogram space and evaluate domain adversarial neural networks (DANN) against baseline and augmentation-based approaches on the OrganAMNIST abdominal CT dataset. Our results demonstrate that baseline models trained only on clean images fail to generalize to images with ring artifacts, and traditional augmentation with other distortion types provides no improvement on unseen artifact domains. In contrast, the DANN approach successfully maintains high classification accuracy on ring artifact images using only unlabeled artifact data during training, demonstrating the viability of domain adaptation for artifact robustness. The domain-adapted model achieved classification performance on ring artifact test data comparable to models explicitly trained with labeled artifact images, while also showing unexpected generalization to uniform noise. These findings provide empirical evidence that domain adaptation can effectively address distribution shift in medical imaging without requiring expensive expert labeling of new artifact distributions, suggesting promise for deployment in clinical settings where novel artifacts may emerge.
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 * @Date: 2025-10-09 23:23:38
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            <a href="https://www.alphaxiv.org/abs/2510.06582v1" target="_blank" rel="noopener noreferrer">
                从激光雷达视角出发：一种面向地面点云分割的特征增强与不确定性感知标注流程
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fei Zhang, Rob Chancia, Josie Clapp, Amirhossein Hassanzadeh, Dimah Dera, Richar...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于激光雷达点云分割技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈无直接关联。论文内容涉及点云处理、特征增强和不确定性建模，这些技术在自动驾驶、机器人导航等视觉应用中更为常见，无法为RecSys/Search/Ads领域提供直接的技术启示或应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 02:25:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06582v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06582v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate semantic segmentation of terrestrial laser scanning (TLS) point clouds is limited by costly manual annotation. We propose a semi-automated, uncertainty-aware pipeline that integrates spherical projection, feature enrichment, ensemble learning, and targeted annotation to reduce labeling effort, while sustaining high accuracy. Our approach projects 3D points to a 2D spherical grid, enriches pixels with multi-source features, and trains an ensemble of segmentation networks to produce pseudo-labels and uncertainty maps, the latter guiding annotation of ambiguous regions. The 2D outputs are back-projected to 3D, yielding densely annotated point clouds supported by a three-tier visualization suite (2D feature maps, 3D colorized point clouds, and compact virtual spheres) for rapid triage and reviewer guidance. Using this pipeline, we build Mangrove3D, a semantic segmentation TLS dataset for mangrove forests. We further evaluate data efficiency and feature importance to address two key questions: (1) how much annotated data are needed and (2) which features matter most. Results show that performance saturates after ~12 annotated scans, geometric features contribute the most, and compact nine-channel stacks capture nearly all discriminative power, with the mean Intersection over Union (mIoU) plateauing at around 0.76. Finally, we confirm the generalization of our feature-enrichment strategy through cross-dataset tests on ForestSemantic and Semantic3D. Our contributions include: (i) a robust, uncertainty-aware TLS annotation pipeline with visualization tools; (ii) the Mangrove3D dataset; and (iii) empirical guidance on data efficiency and feature importance, thus enabling scalable, high-quality segmentation of TLS point clouds for ecological monitoring and beyond. The dataset and processing scripts are publicly available at https://fz-rit.github.io/through-the-lidars-eye/.
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 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.06564v1" target="_blank" rel="noopener noreferrer">
                HSNet：用于单图像超分辨率的异质子图网络
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HSNet: Heterogeneous Subgraph Network for Single Image Super-resolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiongyang Hu, Wenyang Liu, Wenbin Zou, Yuejiao Su, Lap-Pui Chau, Yi Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像超分辨率任务，属于纯粹的视觉处理领域。虽然标题中提到'异质子图网络'，但这是针对图像像素处理的架构改进，与推荐系统、搜索或广告中的异构数据建模没有直接关联。该技术没有明显的应用潜力到RecSys/Search/Ads领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-08 01:32:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.06564v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.06564v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing deep learning approaches for image super-resolution, particularly those based on CNNs and attention mechanisms, often suffer from structural inflexibility. Although graph-based methods offer greater representational adaptability, they are frequently impeded by excessive computational complexity. To overcome these limitations, this paper proposes the Heterogeneous Subgraph Network (HSNet), a novel framework that efficiently leverages graph modeling while maintaining computational feasibility. The core idea of HSNet is to decompose the global graph into manageable sub-components. First, we introduce the Constructive Subgraph Set Block (CSSB), which generates a diverse set of complementary subgraphs. Rather than relying on a single monolithic graph, CSSB captures heterogeneous characteristics of the image by modeling different relational patterns and feature interactions, producing a rich ensemble of both local and global graph structures. Subsequently, the Subgraph Aggregation Block (SAB) integrates the representations embedded across these subgraphs. Through adaptive weighting and fusion of multi-graph features, SAB constructs a comprehensive and discriminative representation that captures intricate interdependencies. Furthermore, a Node Sampling Strategy (NSS) is designed to selectively retain the most salient features, thereby enhancing accuracy while reducing computational overhead. Extensive experiments demonstrate that HSNet achieves state-of-the-art performance, effectively balancing reconstruction quality with computational efficiency. The code will be made publicly available.
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            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>